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            <ul>
<li><a class="reference internal" href="#">API Reference</a><ul>
<li><a class="reference internal" href="#module-sklearn.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code>: Base classes and utility functions</a><ul>
<li><a class="reference internal" href="#base-classes">Base classes</a></li>
<li><a class="reference internal" href="#functions">Functions</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.calibration"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.calibration</span></code>: Probability Calibration</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>: Clustering</a><ul>
<li><a class="reference internal" href="#classes">Classes</a></li>
<li><a class="reference internal" href="#id1">Functions</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.compose"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code>: Composite Estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code>: Covariance Estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.cross_decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cross_decomposition</span></code>: Cross decomposition</a></li>
<li><a class="reference internal" href="#module-sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>: Datasets</a><ul>
<li><a class="reference internal" href="#loaders">Loaders</a></li>
<li><a class="reference internal" href="#samples-generator">Samples generator</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code>: Matrix Decomposition</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.discriminant_analysis"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis</span></code>: Discriminant Analysis</a></li>
<li><a class="reference internal" href="#module-sklearn.dummy"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.dummy</span></code>: Dummy estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code>: Ensemble Methods</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.exceptions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.exceptions</span></code>: Exceptions and warnings</a></li>
<li><a class="reference internal" href="#module-sklearn.experimental"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.experimental</span></code>: Experimental</a></li>
<li><a class="reference internal" href="#module-sklearn.feature_extraction"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction</span></code>: Feature Extraction</a><ul>
<li><a class="reference internal" href="#module-sklearn.feature_extraction.image">From images</a></li>
<li><a class="reference internal" href="#module-sklearn.feature_extraction.text">From text</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.feature_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code>: Feature Selection</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.gaussian_process"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.gaussian_process</span></code>: Gaussian Processes</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.impute"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.impute</span></code>: Impute</a></li>
<li><a class="reference internal" href="#module-sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code>: inspection</a><ul>
<li><a class="reference internal" href="#plotting">Plotting</a><ul>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.isotonic"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.isotonic</span></code>: Isotonic regression</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.kernel_approximation"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_approximation</span></code> Kernel Approximation</a></li>
<li><a class="reference internal" href="#module-sklearn.kernel_ridge"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_ridge</span></code> Kernel Ridge Regression</a></li>
<li><a class="reference internal" href="#module-sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code>: Linear Models</a><ul>
<li><a class="reference internal" href="#linear-classifiers">Linear classifiers</a></li>
<li><a class="reference internal" href="#classical-linear-regressors">Classical linear regressors</a></li>
<li><a class="reference internal" href="#regressors-with-variable-selection">Regressors with variable selection</a></li>
<li><a class="reference internal" href="#bayesian-regressors">Bayesian regressors</a></li>
<li><a class="reference internal" href="#multi-task-linear-regressors-with-variable-selection">Multi-task linear regressors with variable selection</a></li>
<li><a class="reference internal" href="#outlier-robust-regressors">Outlier-robust regressors</a></li>
<li><a class="reference internal" href="#miscellaneous">Miscellaneous</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.manifold"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.manifold</span></code>: Manifold Learning</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#sklearn-metrics-metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code>: Metrics</a><ul>
<li><a class="reference internal" href="#model-selection-interface">Model Selection Interface</a></li>
<li><a class="reference internal" href="#classification-metrics">Classification metrics</a></li>
<li><a class="reference internal" href="#regression-metrics">Regression metrics</a></li>
<li><a class="reference internal" href="#multilabel-ranking-metrics">Multilabel ranking metrics</a></li>
<li><a class="reference internal" href="#clustering-metrics">Clustering metrics</a></li>
<li><a class="reference internal" href="#biclustering-metrics">Biclustering metrics</a></li>
<li><a class="reference internal" href="#pairwise-metrics">Pairwise metrics</a></li>
<li><a class="reference internal" href="#id3">Plotting</a><ul>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.mixture"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.mixture</span></code>: Gaussian Mixture Models</a></li>
<li><a class="reference internal" href="#module-sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code>: Model Selection</a><ul>
<li><a class="reference internal" href="#splitter-classes">Splitter Classes</a></li>
<li><a class="reference internal" href="#splitter-functions">Splitter Functions</a></li>
<li><a class="reference internal" href="#hyper-parameter-optimizers">Hyper-parameter optimizers</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#model-validation">Model validation</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.multiclass"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multiclass</span></code>: Multiclass and multilabel classification</a><ul>
<li><a class="reference internal" href="#multiclass-and-multilabel-classification-strategies">Multiclass and multilabel classification strategies</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.multioutput"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multioutput</span></code>: Multioutput regression and classification</a></li>
<li><a class="reference internal" href="#module-sklearn.naive_bayes"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.naive_bayes</span></code>: Naive Bayes</a></li>
<li><a class="reference internal" href="#module-sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code>: Nearest Neighbors</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.neural_network"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neural_network</span></code>: Neural network models</a></li>
<li><a class="reference internal" href="#module-sklearn.pipeline"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.pipeline</span></code>: Pipeline</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.preprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code>: Preprocessing and Normalization</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.random_projection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.random_projection</span></code>: Random projection</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.semi_supervised"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.semi_supervised</span></code> Semi-Supervised Learning</a></li>
<li><a class="reference internal" href="#module-sklearn.svm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.svm</span></code>: Support Vector Machines</a><ul>
<li><a class="reference internal" href="#estimators">Estimators</a><ul>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.tree"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.tree</span></code>: Decision Trees</a><ul>
<li><a class="reference internal" href="#id4">Plotting</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.utils"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.utils</span></code>: Utilities</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#recently-deprecated">Recently deprecated</a><ul>
<li><a class="reference internal" href="#to-be-removed-in-0-23">To be removed in 0.23</a><ul>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>

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  <div class="section" id="api-reference">
<span id="api-ref"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline">¶</a></h1>
<p>This is the class and function reference of scikit-learn. Please refer to
the <a class="reference internal" href="../user_guide.html#user-guide"><span class="std std-ref">full user guide</span></a> for further details, as the class and
function raw specifications may not be enough to give full guidelines on their
uses.
For reference on concepts repeated across the API, see <a class="reference internal" href="../glossary.html#glossary"><span class="std std-ref">Glossary of Common Terms and API Elements</span></a>.</p>
<div class="section" id="module-sklearn.base">
<span id="sklearn-base-base-classes-and-utility-functions"></span><h2><a class="reference internal" href="#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code></a>: Base classes and utility functions<a class="headerlink" href="#module-sklearn.base" title="Permalink to this headline">¶</a></h2>
<p>Base classes for all estimators.</p>
<p>Used for VotingClassifier</p>
<div class="section" id="base-classes">
<h3>Base classes<a class="headerlink" href="#base-classes" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseEstimator</span></code></a></p></td>
<td><p>Base class for all estimators in scikit-learn</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.BiclusterMixin.html#sklearn.base.BiclusterMixin" title="sklearn.base.BiclusterMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BiclusterMixin</span></code></a></p></td>
<td><p>Mixin class for all bicluster estimators in scikit-learn</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin" title="sklearn.base.ClassifierMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.ClassifierMixin</span></code></a></p></td>
<td><p>Mixin class for all classifiers in scikit-learn.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.ClusterMixin.html#sklearn.base.ClusterMixin" title="sklearn.base.ClusterMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.ClusterMixin</span></code></a></p></td>
<td><p>Mixin class for all cluster estimators in scikit-learn.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.DensityMixin.html#sklearn.base.DensityMixin" title="sklearn.base.DensityMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.DensityMixin</span></code></a></p></td>
<td><p>Mixin class for all density estimators in scikit-learn.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.RegressorMixin.html#sklearn.base.RegressorMixin" title="sklearn.base.RegressorMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.RegressorMixin</span></code></a></p></td>
<td><p>Mixin class for all regression estimators in scikit-learn.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.TransformerMixin.html#sklearn.base.TransformerMixin" title="sklearn.base.TransformerMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.TransformerMixin</span></code></a></p></td>
<td><p>Mixin class for all transformers in scikit-learn.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="functions">
<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.clone.html#sklearn.base.clone" title="sklearn.base.clone"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.clone</span></code></a>(estimator[, safe])</p></td>
<td><p>Constructs a new estimator with the same parameters.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.is_classifier.html#sklearn.base.is_classifier" title="sklearn.base.is_classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.is_classifier</span></code></a>(estimator)</p></td>
<td><p>Return True if the given estimator is (probably) a classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.is_regressor.html#sklearn.base.is_regressor" title="sklearn.base.is_regressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.is_regressor</span></code></a>(estimator)</p></td>
<td><p>Return True if the given estimator is (probably) a regressor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.config_context.html#sklearn.config_context" title="sklearn.config_context"><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_context</span></code></a>(\*\*new_config)</p></td>
<td><p>Context manager for global scikit-learn configuration</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.get_config.html#sklearn.get_config" title="sklearn.get_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config</span></code></a>()</p></td>
<td><p>Retrieve current values for configuration set by <a class="reference internal" href="generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_config</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_config</span></code></a>([assume_finite, working_memory, …])</p></td>
<td><p>Set global scikit-learn configuration</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.show_versions.html#sklearn.show_versions" title="sklearn.show_versions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show_versions</span></code></a>()</p></td>
<td><p>Print useful debugging information</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.calibration">
<span id="sklearn-calibration-probability-calibration"></span><span id="calibration-ref"></span><h2><a class="reference internal" href="#module-sklearn.calibration" title="sklearn.calibration"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.calibration</span></code></a>: Probability Calibration<a class="headerlink" href="#module-sklearn.calibration" title="Permalink to this headline">¶</a></h2>
<p>Calibration of predicted probabilities.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="calibration.html#calibration"><span class="std std-ref">Probability calibration</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calibration.CalibratedClassifierCV</span></code></a>([…])</p></td>
<td><p>Probability calibration with isotonic regression or sigmoid.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.calibration.calibration_curve.html#sklearn.calibration.calibration_curve" title="sklearn.calibration.calibration_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calibration.calibration_curve</span></code></a>(y_true, y_prob)</p></td>
<td><p>Compute true and predicted probabilities for a calibration curve.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.cluster">
<span id="sklearn-cluster-clustering"></span><span id="cluster-ref"></span><h2><a class="reference internal" href="#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>: Clustering<a class="headerlink" href="#module-sklearn.cluster" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a> module gathers popular unsupervised clustering
algorithms.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="clustering.html#clustering"><span class="std std-ref">Clustering</span></a> and <a class="reference internal" href="biclustering.html#biclustering"><span class="std std-ref">Biclustering</span></a> sections for
further details.</p>
<div class="section" id="classes">
<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.AffinityPropagation.html#sklearn.cluster.AffinityPropagation" title="sklearn.cluster.AffinityPropagation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.AffinityPropagation</span></code></a>([damping, …])</p></td>
<td><p>Perform Affinity Propagation Clustering of data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering" title="sklearn.cluster.AgglomerativeClustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.AgglomerativeClustering</span></code></a>([…])</p></td>
<td><p>Agglomerative Clustering</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.Birch.html#sklearn.cluster.Birch" title="sklearn.cluster.Birch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.Birch</span></code></a>([threshold, branching_factor, …])</p></td>
<td><p>Implements the Birch clustering algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN" title="sklearn.cluster.DBSCAN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.DBSCAN</span></code></a>([eps, min_samples, metric, …])</p></td>
<td><p>Perform DBSCAN clustering from vector array or distance matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.FeatureAgglomeration.html#sklearn.cluster.FeatureAgglomeration" title="sklearn.cluster.FeatureAgglomeration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.FeatureAgglomeration</span></code></a>([n_clusters, …])</p></td>
<td><p>Agglomerate features.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.KMeans</span></code></a>([n_clusters, init, n_init, …])</p></td>
<td><p>K-Means clustering.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster.MiniBatchKMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.MiniBatchKMeans</span></code></a>([n_clusters, init, …])</p></td>
<td><p>Mini-Batch K-Means clustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.MeanShift.html#sklearn.cluster.MeanShift" title="sklearn.cluster.MeanShift"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.MeanShift</span></code></a>([bandwidth, seeds, …])</p></td>
<td><p>Mean shift clustering using a flat kernel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.OPTICS.html#sklearn.cluster.OPTICS" title="sklearn.cluster.OPTICS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.OPTICS</span></code></a>([min_samples, max_eps, …])</p></td>
<td><p>Estimate clustering structure from vector array.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering" title="sklearn.cluster.SpectralClustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.SpectralClustering</span></code></a>([n_clusters, …])</p></td>
<td><p>Apply clustering to a projection of the normalized Laplacian.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.SpectralBiclustering.html#sklearn.cluster.SpectralBiclustering" title="sklearn.cluster.SpectralBiclustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.SpectralBiclustering</span></code></a>([n_clusters, …])</p></td>
<td><p>Spectral biclustering (Kluger, 2003).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.SpectralCoclustering.html#sklearn.cluster.SpectralCoclustering" title="sklearn.cluster.SpectralCoclustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.SpectralCoclustering</span></code></a>([n_clusters, …])</p></td>
<td><p>Spectral Co-Clustering algorithm (Dhillon, 2001).</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="id1">
<h3>Functions<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.affinity_propagation.html#sklearn.cluster.affinity_propagation" title="sklearn.cluster.affinity_propagation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.affinity_propagation</span></code></a>(S[, …])</p></td>
<td><p>Perform Affinity Propagation Clustering of data</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.cluster_optics_dbscan.html#sklearn.cluster.cluster_optics_dbscan" title="sklearn.cluster.cluster_optics_dbscan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.cluster_optics_dbscan</span></code></a>(reachability, …)</p></td>
<td><p>Performs DBSCAN extraction for an arbitrary epsilon.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.cluster_optics_xi.html#sklearn.cluster.cluster_optics_xi" title="sklearn.cluster.cluster_optics_xi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.cluster_optics_xi</span></code></a>(reachability, …)</p></td>
<td><p>Automatically extract clusters according to the Xi-steep method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.compute_optics_graph.html#sklearn.cluster.compute_optics_graph" title="sklearn.cluster.compute_optics_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.compute_optics_graph</span></code></a>(X, min_samples, …)</p></td>
<td><p>Computes the OPTICS reachability graph.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.dbscan.html#sklearn.cluster.dbscan" title="sklearn.cluster.dbscan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.dbscan</span></code></a>(X[, eps, min_samples, …])</p></td>
<td><p>Perform DBSCAN clustering from vector array or distance matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.estimate_bandwidth.html#sklearn.cluster.estimate_bandwidth" title="sklearn.cluster.estimate_bandwidth"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.estimate_bandwidth</span></code></a>(X[, quantile, …])</p></td>
<td><p>Estimate the bandwidth to use with the mean-shift algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.k_means.html#sklearn.cluster.k_means" title="sklearn.cluster.k_means"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.k_means</span></code></a>(X, n_clusters[, …])</p></td>
<td><p>K-means clustering algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.mean_shift.html#sklearn.cluster.mean_shift" title="sklearn.cluster.mean_shift"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.mean_shift</span></code></a>(X[, bandwidth, seeds, …])</p></td>
<td><p>Perform mean shift clustering of data using a flat kernel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.spectral_clustering.html#sklearn.cluster.spectral_clustering" title="sklearn.cluster.spectral_clustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.spectral_clustering</span></code></a>(affinity[, …])</p></td>
<td><p>Apply clustering to a projection of the normalized Laplacian.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.ward_tree.html#sklearn.cluster.ward_tree" title="sklearn.cluster.ward_tree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.ward_tree</span></code></a>(X[, connectivity, …])</p></td>
<td><p>Ward clustering based on a Feature matrix.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.compose">
<span id="sklearn-compose-composite-estimators"></span><span id="compose-ref"></span><h2><a class="reference internal" href="#module-sklearn.compose" title="sklearn.compose"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code></a>: Composite Estimators<a class="headerlink" href="#module-sklearn.compose" title="Permalink to this headline">¶</a></h2>
<p>Meta-estimators for building composite models with transformers</p>
<p>In addition to its current contents, this module will eventually be home to
refurbished versions of Pipeline and FeatureUnion.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="compose.html#combining-estimators"><span class="std std-ref">Pipelines and composite estimators</span></a> section for further
details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose.ColumnTransformer</span></code></a>(transformers[, …])</p></td>
<td><p>Applies transformers to columns of an array or pandas DataFrame.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.compose.TransformedTargetRegressor.html#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose.TransformedTargetRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose.TransformedTargetRegressor</span></code></a>([…])</p></td>
<td><p>Meta-estimator to regress on a transformed target.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.compose.make_column_transformer.html#sklearn.compose.make_column_transformer" title="sklearn.compose.make_column_transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose.make_column_transformer</span></code></a>(…)</p></td>
<td><p>Construct a ColumnTransformer from the given transformers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.compose.make_column_selector.html#sklearn.compose.make_column_selector" title="sklearn.compose.make_column_selector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose.make_column_selector</span></code></a>([pattern, …])</p></td>
<td><p>Create a callable to select columns to be used with <code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnTransformer</span></code>.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.covariance">
<span id="sklearn-covariance-covariance-estimators"></span><span id="covariance-ref"></span><h2><a class="reference internal" href="#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code></a>: Covariance Estimators<a class="headerlink" href="#module-sklearn.covariance" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code></a> module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="covariance.html#covariance"><span class="std std-ref">Covariance estimation</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.EmpiricalCovariance.html#sklearn.covariance.EmpiricalCovariance" title="sklearn.covariance.EmpiricalCovariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.EmpiricalCovariance</span></code></a>([…])</p></td>
<td><p>Maximum likelihood covariance estimator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope" title="sklearn.covariance.EllipticEnvelope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.EllipticEnvelope</span></code></a>([…])</p></td>
<td><p>An object for detecting outliers in a Gaussian distributed dataset.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.GraphicalLasso.html#sklearn.covariance.GraphicalLasso" title="sklearn.covariance.GraphicalLasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.GraphicalLasso</span></code></a>([alpha, mode, …])</p></td>
<td><p>Sparse inverse covariance estimation with an l1-penalized estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.GraphicalLassoCV.html#sklearn.covariance.GraphicalLassoCV" title="sklearn.covariance.GraphicalLassoCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.GraphicalLassoCV</span></code></a>([alphas, …])</p></td>
<td><p>Sparse inverse covariance w/ cross-validated choice of the l1 penalty.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.LedoitWolf.html#sklearn.covariance.LedoitWolf" title="sklearn.covariance.LedoitWolf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.LedoitWolf</span></code></a>([store_precision, …])</p></td>
<td><p>LedoitWolf Estimator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.MinCovDet.html#sklearn.covariance.MinCovDet" title="sklearn.covariance.MinCovDet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.MinCovDet</span></code></a>([store_precision, …])</p></td>
<td><p>Minimum Covariance Determinant (MCD): robust estimator of covariance.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.OAS.html#sklearn.covariance.OAS" title="sklearn.covariance.OAS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.OAS</span></code></a>([store_precision, …])</p></td>
<td><p>Oracle Approximating Shrinkage Estimator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.ShrunkCovariance.html#sklearn.covariance.ShrunkCovariance" title="sklearn.covariance.ShrunkCovariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.ShrunkCovariance</span></code></a>([…])</p></td>
<td><p>Covariance estimator with shrinkage</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.empirical_covariance.html#sklearn.covariance.empirical_covariance" title="sklearn.covariance.empirical_covariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.empirical_covariance</span></code></a>(X[, …])</p></td>
<td><p>Computes the Maximum likelihood covariance estimator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.graphical_lasso.html#sklearn.covariance.graphical_lasso" title="sklearn.covariance.graphical_lasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.graphical_lasso</span></code></a>(emp_cov, alpha[, …])</p></td>
<td><p>l1-penalized covariance estimator</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.ledoit_wolf.html#sklearn.covariance.ledoit_wolf" title="sklearn.covariance.ledoit_wolf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.ledoit_wolf</span></code></a>(X[, assume_centered, …])</p></td>
<td><p>Estimates the shrunk Ledoit-Wolf covariance matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.oas.html#sklearn.covariance.oas" title="sklearn.covariance.oas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.oas</span></code></a>(X[, assume_centered])</p></td>
<td><p>Estimate covariance with the Oracle Approximating Shrinkage algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.shrunk_covariance.html#sklearn.covariance.shrunk_covariance" title="sklearn.covariance.shrunk_covariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.shrunk_covariance</span></code></a>(emp_cov[, …])</p></td>
<td><p>Calculates a covariance matrix shrunk on the diagonal</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.cross_decomposition">
<span id="sklearn-cross-decomposition-cross-decomposition"></span><span id="cross-decomposition-ref"></span><h2><a class="reference internal" href="#module-sklearn.cross_decomposition" title="sklearn.cross_decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cross_decomposition</span></code></a>: Cross decomposition<a class="headerlink" href="#module-sklearn.cross_decomposition" title="Permalink to this headline">¶</a></h2>
<p><strong>User guide:</strong> See the <a class="reference internal" href="cross_decomposition.html#cross-decomposition"><span class="std std-ref">Cross decomposition</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cross_decomposition.CCA.html#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition.CCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_decomposition.CCA</span></code></a>([n_components, …])</p></td>
<td><p>CCA Canonical Correlation Analysis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSCanonical.html#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition.PLSCanonical"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_decomposition.PLSCanonical</span></code></a>([…])</p></td>
<td><p>PLSCanonical implements the 2 blocks canonical PLS of the original Wold algorithm [Tenenhaus 1998] p.204, referred as PLS-C2A in [Wegelin 2000].</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSRegression.html#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition.PLSRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_decomposition.PLSRegression</span></code></a>([…])</p></td>
<td><p>PLS regression</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSSVD.html#sklearn.cross_decomposition.PLSSVD" title="sklearn.cross_decomposition.PLSSVD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_decomposition.PLSSVD</span></code></a>([n_components, …])</p></td>
<td><p>Partial Least Square SVD</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.datasets">
<span id="sklearn-datasets-datasets"></span><span id="datasets-ref"></span><h2><a class="reference internal" href="#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>: Datasets<a class="headerlink" href="#module-sklearn.datasets" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a> module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="../datasets/index.html#datasets"><span class="std std-ref">Dataset loading utilities</span></a> section for further details.</p>
<div class="section" id="loaders">
<h3>Loaders<a class="headerlink" href="#loaders" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.clear_data_home.html#sklearn.datasets.clear_data_home" title="sklearn.datasets.clear_data_home"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.clear_data_home</span></code></a>([data_home])</p></td>
<td><p>Delete all the content of the data home cache.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.dump_svmlight_file.html#sklearn.datasets.dump_svmlight_file" title="sklearn.datasets.dump_svmlight_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.dump_svmlight_file</span></code></a>(X, y, f[, …])</p></td>
<td><p>Dump the dataset in svmlight / libsvm file format.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_20newsgroups</span></code></a>([data_home, …])</p></td>
<td><p>Load the filenames and data from the 20 newsgroups dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_20newsgroups_vectorized.html#sklearn.datasets.fetch_20newsgroups_vectorized" title="sklearn.datasets.fetch_20newsgroups_vectorized"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_20newsgroups_vectorized</span></code></a>([…])</p></td>
<td><p>Load the 20 newsgroups dataset and vectorize it into token counts (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_california_housing.html#sklearn.datasets.fetch_california_housing" title="sklearn.datasets.fetch_california_housing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_california_housing</span></code></a>([…])</p></td>
<td><p>Load the California housing dataset (regression).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_covtype.html#sklearn.datasets.fetch_covtype" title="sklearn.datasets.fetch_covtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_covtype</span></code></a>([data_home, …])</p></td>
<td><p>Load the covertype dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_kddcup99.html#sklearn.datasets.fetch_kddcup99" title="sklearn.datasets.fetch_kddcup99"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_kddcup99</span></code></a>([subset, data_home, …])</p></td>
<td><p>Load the kddcup99 dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_lfw_pairs.html#sklearn.datasets.fetch_lfw_pairs" title="sklearn.datasets.fetch_lfw_pairs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_lfw_pairs</span></code></a>([subset, …])</p></td>
<td><p>Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" title="sklearn.datasets.fetch_lfw_people"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_lfw_people</span></code></a>([data_home, …])</p></td>
<td><p>Load the Labeled Faces in the Wild (LFW) people dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_olivetti_faces.html#sklearn.datasets.fetch_olivetti_faces" title="sklearn.datasets.fetch_olivetti_faces"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_olivetti_faces</span></code></a>([data_home, …])</p></td>
<td><p>Load the Olivetti faces data-set from AT&amp;T (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml" title="sklearn.datasets.fetch_openml"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_openml</span></code></a>([name, version, …])</p></td>
<td><p>Fetch dataset from openml by name or dataset id.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_rcv1.html#sklearn.datasets.fetch_rcv1" title="sklearn.datasets.fetch_rcv1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_rcv1</span></code></a>([data_home, subset, …])</p></td>
<td><p>Load the RCV1 multilabel dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_species_distributions.html#sklearn.datasets.fetch_species_distributions" title="sklearn.datasets.fetch_species_distributions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_species_distributions</span></code></a>([…])</p></td>
<td><p>Loader for species distribution dataset from Phillips et.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.get_data_home.html#sklearn.datasets.get_data_home" title="sklearn.datasets.get_data_home"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.get_data_home</span></code></a>([data_home])</p></td>
<td><p>Return the path of the scikit-learn data dir.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston" title="sklearn.datasets.load_boston"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_boston</span></code></a>([return_X_y])</p></td>
<td><p>Load and return the boston house-prices dataset (regression).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer" title="sklearn.datasets.load_breast_cancer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_breast_cancer</span></code></a>([return_X_y])</p></td>
<td><p>Load and return the breast cancer wisconsin dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes" title="sklearn.datasets.load_diabetes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_diabetes</span></code></a>([return_X_y])</p></td>
<td><p>Load and return the diabetes dataset (regression).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_digits</span></code></a>([n_class, return_X_y])</p></td>
<td><p>Load and return the digits dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_files.html#sklearn.datasets.load_files" title="sklearn.datasets.load_files"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_files</span></code></a>(container_path[, …])</p></td>
<td><p>Load text files with categories as subfolder names.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_iris</span></code></a>([return_X_y])</p></td>
<td><p>Load and return the iris dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud" title="sklearn.datasets.load_linnerud"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_linnerud</span></code></a>([return_X_y])</p></td>
<td><p>Load and return the linnerud dataset (multivariate regression).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_sample_image.html#sklearn.datasets.load_sample_image" title="sklearn.datasets.load_sample_image"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_sample_image</span></code></a>(image_name)</p></td>
<td><p>Load the numpy array of a single sample image</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_sample_images.html#sklearn.datasets.load_sample_images" title="sklearn.datasets.load_sample_images"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_sample_images</span></code></a>()</p></td>
<td><p>Load sample images for image manipulation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_svmlight_file.html#sklearn.datasets.load_svmlight_file" title="sklearn.datasets.load_svmlight_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_svmlight_file</span></code></a>(f[, n_features, …])</p></td>
<td><p>Load datasets in the svmlight / libsvm format into sparse CSR matrix</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_svmlight_files.html#sklearn.datasets.load_svmlight_files" title="sklearn.datasets.load_svmlight_files"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_svmlight_files</span></code></a>(files[, …])</p></td>
<td><p>Load dataset from multiple files in SVMlight format</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine" title="sklearn.datasets.load_wine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_wine</span></code></a>([return_X_y])</p></td>
<td><p>Load and return the wine dataset (classification).</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="samples-generator">
<h3>Samples generator<a class="headerlink" href="#samples-generator" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_biclusters.html#sklearn.datasets.make_biclusters" title="sklearn.datasets.make_biclusters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_biclusters</span></code></a>(shape, n_clusters)</p></td>
<td><p>Generate an array with constant block diagonal structure for biclustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_blobs</span></code></a>([n_samples, n_features, …])</p></td>
<td><p>Generate isotropic Gaussian blobs for clustering.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_checkerboard.html#sklearn.datasets.make_checkerboard" title="sklearn.datasets.make_checkerboard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_checkerboard</span></code></a>(shape, n_clusters)</p></td>
<td><p>Generate an array with block checkerboard structure for biclustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_circles</span></code></a>([n_samples, shuffle, …])</p></td>
<td><p>Make a large circle containing a smaller circle in 2d.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_classification</span></code></a>([n_samples, …])</p></td>
<td><p>Generate a random n-class classification problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_friedman1.html#sklearn.datasets.make_friedman1" title="sklearn.datasets.make_friedman1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_friedman1</span></code></a>([n_samples, …])</p></td>
<td><p>Generate the “Friedman #1” regression problem</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_friedman2.html#sklearn.datasets.make_friedman2" title="sklearn.datasets.make_friedman2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_friedman2</span></code></a>([n_samples, noise, …])</p></td>
<td><p>Generate the “Friedman #2” regression problem</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_friedman3.html#sklearn.datasets.make_friedman3" title="sklearn.datasets.make_friedman3"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_friedman3</span></code></a>([n_samples, noise, …])</p></td>
<td><p>Generate the “Friedman #3” regression problem</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_gaussian_quantiles.html#sklearn.datasets.make_gaussian_quantiles" title="sklearn.datasets.make_gaussian_quantiles"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_gaussian_quantiles</span></code></a>([mean, …])</p></td>
<td><p>Generate isotropic Gaussian and label samples by quantile</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_hastie_10_2.html#sklearn.datasets.make_hastie_10_2" title="sklearn.datasets.make_hastie_10_2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_hastie_10_2</span></code></a>([n_samples, …])</p></td>
<td><p>Generates data for binary classification used in Hastie et al.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_low_rank_matrix</span></code></a>([n_samples, …])</p></td>
<td><p>Generate a mostly low rank matrix with bell-shaped singular values</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_moons</span></code></a>([n_samples, shuffle, …])</p></td>
<td><p>Make two interleaving half circles</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_multilabel_classification.html#sklearn.datasets.make_multilabel_classification" title="sklearn.datasets.make_multilabel_classification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_multilabel_classification</span></code></a>([…])</p></td>
<td><p>Generate a random multilabel classification problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_regression</span></code></a>([n_samples, …])</p></td>
<td><p>Generate a random regression problem.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_s_curve.html#sklearn.datasets.make_s_curve" title="sklearn.datasets.make_s_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_s_curve</span></code></a>([n_samples, noise, …])</p></td>
<td><p>Generate an S curve dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_sparse_coded_signal.html#sklearn.datasets.make_sparse_coded_signal" title="sklearn.datasets.make_sparse_coded_signal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_sparse_coded_signal</span></code></a>(n_samples, …)</p></td>
<td><p>Generate a signal as a sparse combination of dictionary elements.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_sparse_spd_matrix.html#sklearn.datasets.make_sparse_spd_matrix" title="sklearn.datasets.make_sparse_spd_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_sparse_spd_matrix</span></code></a>([dim, …])</p></td>
<td><p>Generate a sparse symmetric definite positive matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_sparse_uncorrelated.html#sklearn.datasets.make_sparse_uncorrelated" title="sklearn.datasets.make_sparse_uncorrelated"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_sparse_uncorrelated</span></code></a>([…])</p></td>
<td><p>Generate a random regression problem with sparse uncorrelated design</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_spd_matrix.html#sklearn.datasets.make_spd_matrix" title="sklearn.datasets.make_spd_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_spd_matrix</span></code></a>(n_dim[, random_state])</p></td>
<td><p>Generate a random symmetric, positive-definite matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_swiss_roll.html#sklearn.datasets.make_swiss_roll" title="sklearn.datasets.make_swiss_roll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_swiss_roll</span></code></a>([n_samples, noise, …])</p></td>
<td><p>Generate a swiss roll dataset.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.decomposition">
<span id="sklearn-decomposition-matrix-decomposition"></span><span id="decomposition-ref"></span><h2><a class="reference internal" href="#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a>: Matrix Decomposition<a class="headerlink" href="#module-sklearn.decomposition" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a> module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="decomposition.html#decompositions"><span class="std std-ref">Decomposing signals in components (matrix factorization problems)</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.DictionaryLearning.html#sklearn.decomposition.DictionaryLearning" title="sklearn.decomposition.DictionaryLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.DictionaryLearning</span></code></a>([…])</p></td>
<td><p>Dictionary learning</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.FactorAnalysis.html#sklearn.decomposition.FactorAnalysis" title="sklearn.decomposition.FactorAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.FactorAnalysis</span></code></a>([n_components, …])</p></td>
<td><p>Factor Analysis (FA)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.FastICA.html#sklearn.decomposition.FastICA" title="sklearn.decomposition.FastICA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.FastICA</span></code></a>([n_components, …])</p></td>
<td><p>FastICA: a fast algorithm for Independent Component Analysis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.IncrementalPCA.html#sklearn.decomposition.IncrementalPCA" title="sklearn.decomposition.IncrementalPCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.IncrementalPCA</span></code></a>([n_components, …])</p></td>
<td><p>Incremental principal components analysis (IPCA).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.KernelPCA.html#sklearn.decomposition.KernelPCA" title="sklearn.decomposition.KernelPCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.KernelPCA</span></code></a>([n_components, …])</p></td>
<td><p>Kernel Principal component analysis (KPCA)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.LatentDirichletAllocation.html#sklearn.decomposition.LatentDirichletAllocation" title="sklearn.decomposition.LatentDirichletAllocation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.LatentDirichletAllocation</span></code></a>([…])</p></td>
<td><p>Latent Dirichlet Allocation with online variational Bayes algorithm</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.MiniBatchDictionaryLearning.html#sklearn.decomposition.MiniBatchDictionaryLearning" title="sklearn.decomposition.MiniBatchDictionaryLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.MiniBatchDictionaryLearning</span></code></a>([…])</p></td>
<td><p>Mini-batch dictionary learning</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.MiniBatchSparsePCA.html#sklearn.decomposition.MiniBatchSparsePCA" title="sklearn.decomposition.MiniBatchSparsePCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.MiniBatchSparsePCA</span></code></a>([…])</p></td>
<td><p>Mini-batch Sparse Principal Components Analysis</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF" title="sklearn.decomposition.NMF"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.NMF</span></code></a>([n_components, init, …])</p></td>
<td><p>Non-Negative Matrix Factorization (NMF)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.PCA</span></code></a>([n_components, copy, …])</p></td>
<td><p>Principal component analysis (PCA).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.SparsePCA.html#sklearn.decomposition.SparsePCA" title="sklearn.decomposition.SparsePCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.SparsePCA</span></code></a>([n_components, …])</p></td>
<td><p>Sparse Principal Components Analysis (SparsePCA)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.SparseCoder.html#sklearn.decomposition.SparseCoder" title="sklearn.decomposition.SparseCoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.SparseCoder</span></code></a>(dictionary[, …])</p></td>
<td><p>Sparse coding</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.TruncatedSVD.html#sklearn.decomposition.TruncatedSVD" title="sklearn.decomposition.TruncatedSVD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.TruncatedSVD</span></code></a>([n_components, …])</p></td>
<td><p>Dimensionality reduction using truncated SVD (aka LSA).</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.dict_learning.html#sklearn.decomposition.dict_learning" title="sklearn.decomposition.dict_learning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.dict_learning</span></code></a>(X, n_components, …)</p></td>
<td><p>Solves a dictionary learning matrix factorization problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.dict_learning_online.html#sklearn.decomposition.dict_learning_online" title="sklearn.decomposition.dict_learning_online"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.dict_learning_online</span></code></a>(X[, …])</p></td>
<td><p>Solves a dictionary learning matrix factorization problem online.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.fastica.html#sklearn.decomposition.fastica" title="sklearn.decomposition.fastica"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.fastica</span></code></a>(X[, n_components, …])</p></td>
<td><p>Perform Fast Independent Component Analysis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.non_negative_factorization.html#sklearn.decomposition.non_negative_factorization" title="sklearn.decomposition.non_negative_factorization"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.non_negative_factorization</span></code></a>(X)</p></td>
<td><p>Compute Non-negative Matrix Factorization (NMF)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.sparse_encode.html#sklearn.decomposition.sparse_encode" title="sklearn.decomposition.sparse_encode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.sparse_encode</span></code></a>(X, dictionary[, …])</p></td>
<td><p>Sparse coding</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.discriminant_analysis">
<span id="sklearn-discriminant-analysis-discriminant-analysis"></span><span id="lda-ref"></span><h2><a class="reference internal" href="#module-sklearn.discriminant_analysis" title="sklearn.discriminant_analysis"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis</span></code></a>: Discriminant Analysis<a class="headerlink" href="#module-sklearn.discriminant_analysis" title="Permalink to this headline">¶</a></h2>
<p>Linear Discriminant Analysis and Quadratic Discriminant Analysis</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="lda_qda.html#lda-qda"><span class="std std-ref">Linear and Quadratic Discriminant Analysis</span></a> section for further details.</p>
<table class="longtable docutils align-default">
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<col style="width: 10%" />
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<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis</span></code></a>([…])</p></td>
<td><p>Linear Discriminant Analysis</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">discriminant_analysis.QuadraticDiscriminantAnalysis</span></code></a>([…])</p></td>
<td><p>Quadratic Discriminant Analysis</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.dummy">
<span id="sklearn-dummy-dummy-estimators"></span><span id="dummy-ref"></span><h2><a class="reference internal" href="#module-sklearn.dummy" title="sklearn.dummy"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.dummy</span></code></a>: Dummy estimators<a class="headerlink" href="#module-sklearn.dummy" title="Permalink to this headline">¶</a></h2>
<p><strong>User guide:</strong> See the <a class="reference internal" href="model_evaluation.html#model-evaluation"><span class="std std-ref">Metrics and scoring: quantifying the quality of predictions</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy.DummyClassifier</span></code></a>([strategy, …])</p></td>
<td><p>DummyClassifier is a classifier that makes predictions using simple rules.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.dummy.DummyRegressor.html#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy.DummyRegressor</span></code></a>([strategy, constant, …])</p></td>
<td><p>DummyRegressor is a regressor that makes predictions using simple rules.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
</tbody>
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</div>
<div class="section" id="module-sklearn.ensemble">
<span id="sklearn-ensemble-ensemble-methods"></span><span id="ensemble-ref"></span><h2><a class="reference internal" href="#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a>: Ensemble Methods<a class="headerlink" href="#module-sklearn.ensemble" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a> module includes ensemble-based methods for
classification, regression and anomaly detection.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="ensemble.html#ensemble"><span class="std std-ref">Ensemble methods</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble.AdaBoostClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.AdaBoostClassifier</span></code></a>([…])</p></td>
<td><p>An AdaBoost classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.AdaBoostRegressor</span></code></a>([base_estimator, …])</p></td>
<td><p>An AdaBoost regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble.BaggingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.BaggingClassifier</span></code></a>([base_estimator, …])</p></td>
<td><p>A Bagging classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble.BaggingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.BaggingRegressor</span></code></a>([base_estimator, …])</p></td>
<td><p>A Bagging regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble.ExtraTreesClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.ExtraTreesClassifier</span></code></a>([…])</p></td>
<td><p>An extra-trees classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble.ExtraTreesRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.ExtraTreesRegressor</span></code></a>([n_estimators, …])</p></td>
<td><p>An extra-trees regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.GradientBoostingClassifier</span></code></a>([loss, …])</p></td>
<td><p>Gradient Boosting for classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.GradientBoostingRegressor</span></code></a>([loss, …])</p></td>
<td><p>Gradient Boosting for regression.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest" title="sklearn.ensemble.IsolationForest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.IsolationForest</span></code></a>([n_estimators, …])</p></td>
<td><p>Isolation Forest Algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.RandomForestClassifier</span></code></a>([…])</p></td>
<td><p>A random forest classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.RandomForestRegressor</span></code></a>([…])</p></td>
<td><p>A random forest regressor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble.RandomTreesEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.RandomTreesEmbedding</span></code></a>([…])</p></td>
<td><p>An ensemble of totally random trees.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.StackingClassifier.html#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble.StackingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.StackingClassifier</span></code></a>(estimators[, …])</p></td>
<td><p>Stack of estimators with a final classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.StackingRegressor.html#sklearn.ensemble.StackingRegressor" title="sklearn.ensemble.StackingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.StackingRegressor</span></code></a>(estimators[, …])</p></td>
<td><p>Stack of estimators with a final regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.VotingClassifier</span></code></a>(estimators[, …])</p></td>
<td><p>Soft Voting/Majority Rule classifier for unfitted estimators.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.VotingRegressor.html#sklearn.ensemble.VotingRegressor" title="sklearn.ensemble.VotingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.VotingRegressor</span></code></a>(estimators[, …])</p></td>
<td><p>Prediction voting regressor for unfitted estimators.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.HistGradientBoostingRegressor</span></code></a>([…])</p></td>
<td><p>Histogram-based Gradient Boosting Regression Tree.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.HistGradientBoostingClassifier</span></code></a>([…])</p></td>
<td><p>Histogram-based Gradient Boosting Classification Tree.</p></td>
</tr>
</tbody>
</table>
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<div class="section" id="module-sklearn.exceptions">
<span id="sklearn-exceptions-exceptions-and-warnings"></span><span id="exceptions-ref"></span><h2><a class="reference internal" href="#module-sklearn.exceptions" title="sklearn.exceptions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.exceptions</span></code></a>: Exceptions and warnings<a class="headerlink" href="#module-sklearn.exceptions" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.exceptions" title="sklearn.exceptions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.exceptions</span></code></a> module includes all custom warnings and error
classes used across scikit-learn.</p>
<table class="longtable docutils align-default">
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<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.ChangedBehaviorWarning.html#sklearn.exceptions.ChangedBehaviorWarning" title="sklearn.exceptions.ChangedBehaviorWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.ChangedBehaviorWarning</span></code></a></p></td>
<td><p>Warning class used to notify the user of any change in the behavior.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.exceptions.ConvergenceWarning.html#sklearn.exceptions.ConvergenceWarning" title="sklearn.exceptions.ConvergenceWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.ConvergenceWarning</span></code></a></p></td>
<td><p>Custom warning to capture convergence problems</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.DataConversionWarning.html#sklearn.exceptions.DataConversionWarning" title="sklearn.exceptions.DataConversionWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.DataConversionWarning</span></code></a></p></td>
<td><p>Warning used to notify implicit data conversions happening in the code.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.exceptions.DataDimensionalityWarning.html#sklearn.exceptions.DataDimensionalityWarning" title="sklearn.exceptions.DataDimensionalityWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.DataDimensionalityWarning</span></code></a></p></td>
<td><p>Custom warning to notify potential issues with data dimensionality.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.EfficiencyWarning.html#sklearn.exceptions.EfficiencyWarning" title="sklearn.exceptions.EfficiencyWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.EfficiencyWarning</span></code></a></p></td>
<td><p>Warning used to notify the user of inefficient computation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.exceptions.FitFailedWarning.html#sklearn.exceptions.FitFailedWarning" title="sklearn.exceptions.FitFailedWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.FitFailedWarning</span></code></a></p></td>
<td><p>Warning class used if there is an error while fitting the estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.NotFittedError.html#sklearn.exceptions.NotFittedError" title="sklearn.exceptions.NotFittedError"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.NotFittedError</span></code></a></p></td>
<td><p>Exception class to raise if estimator is used before fitting.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.exceptions.NonBLASDotWarning.html#sklearn.exceptions.NonBLASDotWarning" title="sklearn.exceptions.NonBLASDotWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.NonBLASDotWarning</span></code></a></p></td>
<td><p>Warning used when the dot operation does not use BLAS.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.UndefinedMetricWarning.html#sklearn.exceptions.UndefinedMetricWarning" title="sklearn.exceptions.UndefinedMetricWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.UndefinedMetricWarning</span></code></a></p></td>
<td><p>Warning used when the metric is invalid</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.experimental">
<span id="sklearn-experimental-experimental"></span><h2><a class="reference internal" href="#module-sklearn.experimental" title="sklearn.experimental"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.experimental</span></code></a>: Experimental<a class="headerlink" href="#module-sklearn.experimental" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.experimental" title="sklearn.experimental"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.experimental</span></code></a> module provides importable modules that enable
the use of experimental features or estimators.</p>
<p>The features and estimators that are experimental aren’t subject to
deprecation cycles. Use them at your own risks!</p>
<table class="longtable docutils align-default">
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</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.experimental.enable_hist_gradient_boosting.html#module-sklearn.experimental.enable_hist_gradient_boosting" title="sklearn.experimental.enable_hist_gradient_boosting"><code class="xref py py-obj docutils literal notranslate"><span class="pre">experimental.enable_hist_gradient_boosting</span></code></a></p></td>
<td><p>Enables histogram-based gradient boosting estimators.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.experimental.enable_iterative_imputer.html#module-sklearn.experimental.enable_iterative_imputer" title="sklearn.experimental.enable_iterative_imputer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">experimental.enable_iterative_imputer</span></code></a></p></td>
<td><p>Enables IterativeImputer</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.feature_extraction">
<span id="sklearn-feature-extraction-feature-extraction"></span><span id="feature-extraction-ref"></span><h2><a class="reference internal" href="#module-sklearn.feature_extraction" title="sklearn.feature_extraction"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction</span></code></a>: Feature Extraction<a class="headerlink" href="#module-sklearn.feature_extraction" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.feature_extraction" title="sklearn.feature_extraction"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction</span></code></a> module deals with feature extraction
from raw data. It currently includes methods to extract features from text and
images.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="feature_extraction.html#feature-extraction"><span class="std std-ref">Feature extraction</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.DictVectorizer.html#sklearn.feature_extraction.DictVectorizer" title="sklearn.feature_extraction.DictVectorizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.DictVectorizer</span></code></a>([dtype, …])</p></td>
<td><p>Transforms lists of feature-value mappings to vectors.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher" title="sklearn.feature_extraction.FeatureHasher"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.FeatureHasher</span></code></a>([…])</p></td>
<td><p>Implements feature hashing, aka the hashing trick.</p></td>
</tr>
</tbody>
</table>
<div class="section" id="module-sklearn.feature_extraction.image">
<span id="from-images"></span><h3>From images<a class="headerlink" href="#module-sklearn.feature_extraction.image" title="Permalink to this headline">¶</a></h3>
<p>The <a class="reference internal" href="#module-sklearn.feature_extraction.image" title="sklearn.feature_extraction.image"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction.image</span></code></a> submodule gathers utilities to
extract features from images.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.image.extract_patches_2d.html#sklearn.feature_extraction.image.extract_patches_2d" title="sklearn.feature_extraction.image.extract_patches_2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.image.extract_patches_2d</span></code></a>(…)</p></td>
<td><p>Reshape a 2D image into a collection of patches</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.image.grid_to_graph.html#sklearn.feature_extraction.image.grid_to_graph" title="sklearn.feature_extraction.image.grid_to_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.image.grid_to_graph</span></code></a>(n_x, n_y)</p></td>
<td><p>Graph of the pixel-to-pixel connections</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.image.img_to_graph.html#sklearn.feature_extraction.image.img_to_graph" title="sklearn.feature_extraction.image.img_to_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.image.img_to_graph</span></code></a>(img[, …])</p></td>
<td><p>Graph of the pixel-to-pixel gradient connections</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.image.reconstruct_from_patches_2d.html#sklearn.feature_extraction.image.reconstruct_from_patches_2d" title="sklearn.feature_extraction.image.reconstruct_from_patches_2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.image.reconstruct_from_patches_2d</span></code></a>(…)</p></td>
<td><p>Reconstruct the image from all of its patches.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.image.PatchExtractor.html#sklearn.feature_extraction.image.PatchExtractor" title="sklearn.feature_extraction.image.PatchExtractor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.image.PatchExtractor</span></code></a>([…])</p></td>
<td><p>Extracts patches from a collection of images</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.feature_extraction.text">
<span id="from-text"></span><span id="text-feature-extraction-ref"></span><h3>From text<a class="headerlink" href="#module-sklearn.feature_extraction.text" title="Permalink to this headline">¶</a></h3>
<p>The <a class="reference internal" href="#module-sklearn.feature_extraction.text" title="sklearn.feature_extraction.text"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction.text</span></code></a> submodule gathers utilities to
build feature vectors from text documents.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.text.CountVectorizer</span></code></a>([…])</p></td>
<td><p>Convert a collection of text documents to a matrix of token counts</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.text.HashingVectorizer.html#sklearn.feature_extraction.text.HashingVectorizer" title="sklearn.feature_extraction.text.HashingVectorizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.text.HashingVectorizer</span></code></a>([…])</p></td>
<td><p>Convert a collection of text documents to a matrix of token occurrences</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer" title="sklearn.feature_extraction.text.TfidfTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.text.TfidfTransformer</span></code></a>([…])</p></td>
<td><p>Transform a count matrix to a normalized tf or tf-idf representation</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer" title="sklearn.feature_extraction.text.TfidfVectorizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_extraction.text.TfidfVectorizer</span></code></a>([…])</p></td>
<td><p>Convert a collection of raw documents to a matrix of TF-IDF features.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.feature_selection">
<span id="sklearn-feature-selection-feature-selection"></span><span id="feature-selection-ref"></span><h2><a class="reference internal" href="#module-sklearn.feature_selection" title="sklearn.feature_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code></a>: Feature Selection<a class="headerlink" href="#module-sklearn.feature_selection" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.feature_selection" title="sklearn.feature_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code></a> module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="feature_selection.html#feature-selection"><span class="std std-ref">Feature selection</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.GenericUnivariateSelect.html#sklearn.feature_selection.GenericUnivariateSelect" title="sklearn.feature_selection.GenericUnivariateSelect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.GenericUnivariateSelect</span></code></a>([…])</p></td>
<td><p>Univariate feature selector with configurable strategy.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.SelectPercentile.html#sklearn.feature_selection.SelectPercentile" title="sklearn.feature_selection.SelectPercentile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.SelectPercentile</span></code></a>([…])</p></td>
<td><p>Select features according to a percentile of the highest scores.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.SelectKBest</span></code></a>([score_func, k])</p></td>
<td><p>Select features according to the k highest scores.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.SelectFpr.html#sklearn.feature_selection.SelectFpr" title="sklearn.feature_selection.SelectFpr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.SelectFpr</span></code></a>([score_func, alpha])</p></td>
<td><p>Filter: Select the pvalues below alpha based on a FPR test.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.SelectFdr.html#sklearn.feature_selection.SelectFdr" title="sklearn.feature_selection.SelectFdr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.SelectFdr</span></code></a>([score_func, alpha])</p></td>
<td><p>Filter: Select the p-values for an estimated false discovery rate</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel" title="sklearn.feature_selection.SelectFromModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.SelectFromModel</span></code></a>(estimator)</p></td>
<td><p>Meta-transformer for selecting features based on importance weights.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.SelectFwe.html#sklearn.feature_selection.SelectFwe" title="sklearn.feature_selection.SelectFwe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.SelectFwe</span></code></a>([score_func, alpha])</p></td>
<td><p>Filter: Select the p-values corresponding to Family-wise error rate</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.RFE.html#sklearn.feature_selection.RFE" title="sklearn.feature_selection.RFE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.RFE</span></code></a>(estimator[, …])</p></td>
<td><p>Feature ranking with recursive feature elimination.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.RFECV.html#sklearn.feature_selection.RFECV" title="sklearn.feature_selection.RFECV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.RFECV</span></code></a>(estimator[, step, …])</p></td>
<td><p>Feature ranking with recursive feature elimination and cross-validated selection of the best number of features.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.VarianceThreshold.html#sklearn.feature_selection.VarianceThreshold" title="sklearn.feature_selection.VarianceThreshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.VarianceThreshold</span></code></a>([threshold])</p></td>
<td><p>Feature selector that removes all low-variance features.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.chi2.html#sklearn.feature_selection.chi2" title="sklearn.feature_selection.chi2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.chi2</span></code></a>(X, y)</p></td>
<td><p>Compute chi-squared stats between each non-negative feature and class.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.f_classif.html#sklearn.feature_selection.f_classif" title="sklearn.feature_selection.f_classif"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.f_classif</span></code></a>(X, y)</p></td>
<td><p>Compute the ANOVA F-value for the provided sample.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.f_regression.html#sklearn.feature_selection.f_regression" title="sklearn.feature_selection.f_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.f_regression</span></code></a>(X, y[, center])</p></td>
<td><p>Univariate linear regression tests.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.mutual_info_classif.html#sklearn.feature_selection.mutual_info_classif" title="sklearn.feature_selection.mutual_info_classif"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.mutual_info_classif</span></code></a>(X, y)</p></td>
<td><p>Estimate mutual information for a discrete target variable.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.mutual_info_regression.html#sklearn.feature_selection.mutual_info_regression" title="sklearn.feature_selection.mutual_info_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.mutual_info_regression</span></code></a>(X, y)</p></td>
<td><p>Estimate mutual information for a continuous target variable.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.gaussian_process">
<span id="sklearn-gaussian-process-gaussian-processes"></span><span id="gaussian-process-ref"></span><h2><a class="reference internal" href="#module-sklearn.gaussian_process" title="sklearn.gaussian_process"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.gaussian_process</span></code></a>: Gaussian Processes<a class="headerlink" href="#module-sklearn.gaussian_process" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.gaussian_process" title="sklearn.gaussian_process"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.gaussian_process</span></code></a> module implements Gaussian Process
based regression and classification.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="gaussian_process.html#gaussian-process"><span class="std std-ref">Gaussian Processes</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process.GaussianProcessClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.GaussianProcessClassifier</span></code></a>([…])</p></td>
<td><p>Gaussian process classification (GPC) based on Laplace approximation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor" title="sklearn.gaussian_process.GaussianProcessRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.GaussianProcessRegressor</span></code></a>([…])</p></td>
<td><p>Gaussian process regression (GPR).</p></td>
</tr>
</tbody>
</table>
<p>Kernels:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.CompoundKernel.html#sklearn.gaussian_process.kernels.CompoundKernel" title="sklearn.gaussian_process.kernels.CompoundKernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.CompoundKernel</span></code></a>(kernels)</p></td>
<td><p>Kernel which is composed of a set of other kernels.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.ConstantKernel.html#sklearn.gaussian_process.kernels.ConstantKernel" title="sklearn.gaussian_process.kernels.ConstantKernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.ConstantKernel</span></code></a>([…])</p></td>
<td><p>Constant kernel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.DotProduct.html#sklearn.gaussian_process.kernels.DotProduct" title="sklearn.gaussian_process.kernels.DotProduct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.DotProduct</span></code></a>([…])</p></td>
<td><p>Dot-Product kernel.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.ExpSineSquared.html#sklearn.gaussian_process.kernels.ExpSineSquared" title="sklearn.gaussian_process.kernels.ExpSineSquared"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.ExpSineSquared</span></code></a>([…])</p></td>
<td><p>Exp-Sine-Squared kernel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.Exponentiation.html#sklearn.gaussian_process.kernels.Exponentiation" title="sklearn.gaussian_process.kernels.Exponentiation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.Exponentiation</span></code></a>(…)</p></td>
<td><p>Exponentiate kernel by given exponent.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.Hyperparameter.html#sklearn.gaussian_process.kernels.Hyperparameter" title="sklearn.gaussian_process.kernels.Hyperparameter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.Hyperparameter</span></code></a></p></td>
<td><p>A kernel hyperparameter’s specification in form of a namedtuple.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.Kernel.html#sklearn.gaussian_process.kernels.Kernel" title="sklearn.gaussian_process.kernels.Kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.Kernel</span></code></a></p></td>
<td><p>Base class for all kernels.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.Matern.html#sklearn.gaussian_process.kernels.Matern" title="sklearn.gaussian_process.kernels.Matern"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.Matern</span></code></a>([…])</p></td>
<td><p>Matern kernel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.PairwiseKernel.html#sklearn.gaussian_process.kernels.PairwiseKernel" title="sklearn.gaussian_process.kernels.PairwiseKernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.PairwiseKernel</span></code></a>([…])</p></td>
<td><p>Wrapper for kernels in sklearn.metrics.pairwise.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.Product.html#sklearn.gaussian_process.kernels.Product" title="sklearn.gaussian_process.kernels.Product"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.Product</span></code></a>(k1, k2)</p></td>
<td><p>Product-kernel k1 * k2 of two kernels k1 and k2.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.RBF.html#sklearn.gaussian_process.kernels.RBF" title="sklearn.gaussian_process.kernels.RBF"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.RBF</span></code></a>([length_scale, …])</p></td>
<td><p>Radial-basis function kernel (aka squared-exponential kernel).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.RationalQuadratic.html#sklearn.gaussian_process.kernels.RationalQuadratic" title="sklearn.gaussian_process.kernels.RationalQuadratic"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.RationalQuadratic</span></code></a>([…])</p></td>
<td><p>Rational Quadratic kernel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.Sum.html#sklearn.gaussian_process.kernels.Sum" title="sklearn.gaussian_process.kernels.Sum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.Sum</span></code></a>(k1, k2)</p></td>
<td><p>Sum-kernel k1 + k2 of two kernels k1 and k2.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.gaussian_process.kernels.WhiteKernel.html#sklearn.gaussian_process.kernels.WhiteKernel" title="sklearn.gaussian_process.kernels.WhiteKernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process.kernels.WhiteKernel</span></code></a>([…])</p></td>
<td><p>White kernel.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.impute">
<span id="sklearn-impute-impute"></span><span id="impute-ref"></span><h2><a class="reference internal" href="#module-sklearn.impute" title="sklearn.impute"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.impute</span></code></a>: Impute<a class="headerlink" href="#module-sklearn.impute" title="Permalink to this headline">¶</a></h2>
<p>Transformers for missing value imputation</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="impute.html#impute"><span class="std std-ref">Imputation of missing values</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">impute.SimpleImputer</span></code></a>([missing_values, …])</p></td>
<td><p>Imputation transformer for completing missing values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer" title="sklearn.impute.IterativeImputer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">impute.IterativeImputer</span></code></a>([estimator, …])</p></td>
<td><p>Multivariate imputer that estimates each feature from all the others.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.impute.MissingIndicator.html#sklearn.impute.MissingIndicator" title="sklearn.impute.MissingIndicator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">impute.MissingIndicator</span></code></a>([missing_values, …])</p></td>
<td><p>Binary indicators for missing values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.impute.KNNImputer.html#sklearn.impute.KNNImputer" title="sklearn.impute.KNNImputer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">impute.KNNImputer</span></code></a>([missing_values, …])</p></td>
<td><p>Imputation for completing missing values using k-Nearest Neighbors.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.inspection">
<span id="sklearn-inspection-inspection"></span><span id="inspection-ref"></span><h2><a class="reference internal" href="#module-sklearn.inspection" title="sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a>: inspection<a class="headerlink" href="#module-sklearn.inspection" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.inspection" title="sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a> module includes tools for model inspection.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.inspection.partial_dependence.html#sklearn.inspection.partial_dependence" title="sklearn.inspection.partial_dependence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inspection.partial_dependence</span></code></a>(estimator, X, …)</p></td>
<td><p>Partial dependence of <code class="docutils literal notranslate"><span class="pre">features</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.inspection.permutation_importance.html#sklearn.inspection.permutation_importance" title="sklearn.inspection.permutation_importance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inspection.permutation_importance</span></code></a>(estimator, …)</p></td>
<td><p>Permutation importance for feature evaluation <a class="reference internal" href="generated/sklearn.inspection.permutation_importance.html#rd9e56ef97513-bre" id="id2"><span>[Rd9e56ef97513-BRE]</span></a>.</p></td>
</tr>
</tbody>
</table>
<div class="section" id="plotting">
<h3>Plotting<a class="headerlink" href="#plotting" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay" title="sklearn.inspection.PartialDependenceDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inspection.PartialDependenceDisplay</span></code></a>(…)</p></td>
<td><p>Partial Dependence Plot (PDP) visualization.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.inspection.plot_partial_dependence.html#sklearn.inspection.plot_partial_dependence" title="sklearn.inspection.plot_partial_dependence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inspection.plot_partial_dependence</span></code></a>(…[, …])</p></td>
<td><p>Partial dependence plots.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.isotonic">
<span id="sklearn-isotonic-isotonic-regression"></span><span id="isotonic-ref"></span><h2><a class="reference internal" href="#module-sklearn.isotonic" title="sklearn.isotonic"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.isotonic</span></code></a>: Isotonic regression<a class="headerlink" href="#module-sklearn.isotonic" title="Permalink to this headline">¶</a></h2>
<p><strong>User guide:</strong> See the <a class="reference internal" href="isotonic.html#isotonic"><span class="std std-ref">Isotonic regression</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.isotonic.IsotonicRegression.html#sklearn.isotonic.IsotonicRegression" title="sklearn.isotonic.IsotonicRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isotonic.IsotonicRegression</span></code></a>([y_min, y_max, …])</p></td>
<td><p>Isotonic regression model.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.isotonic.check_increasing.html#sklearn.isotonic.check_increasing" title="sklearn.isotonic.check_increasing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isotonic.check_increasing</span></code></a>(x, y)</p></td>
<td><p>Determine whether y is monotonically correlated with x.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.isotonic.isotonic_regression.html#sklearn.isotonic.isotonic_regression" title="sklearn.isotonic.isotonic_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isotonic.isotonic_regression</span></code></a>(y[, …])</p></td>
<td><p>Solve the isotonic regression model.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.kernel_approximation">
<span id="sklearn-kernel-approximation-kernel-approximation"></span><span id="kernel-approximation-ref"></span><h2><a class="reference internal" href="#module-sklearn.kernel_approximation" title="sklearn.kernel_approximation"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_approximation</span></code></a> Kernel Approximation<a class="headerlink" href="#module-sklearn.kernel_approximation" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.kernel_approximation" title="sklearn.kernel_approximation"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_approximation</span></code></a> module implements several
approximate kernel feature maps base on Fourier transforms.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="kernel_approximation.html#kernel-approximation"><span class="std std-ref">Kernel Approximation</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.kernel_approximation.AdditiveChi2Sampler.html#sklearn.kernel_approximation.AdditiveChi2Sampler" title="sklearn.kernel_approximation.AdditiveChi2Sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kernel_approximation.AdditiveChi2Sampler</span></code></a>([…])</p></td>
<td><p>Approximate feature map for additive chi2 kernel.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.kernel_approximation.Nystroem.html#sklearn.kernel_approximation.Nystroem" title="sklearn.kernel_approximation.Nystroem"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kernel_approximation.Nystroem</span></code></a>([kernel, …])</p></td>
<td><p>Approximate a kernel map using a subset of the training data.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.kernel_approximation.RBFSampler.html#sklearn.kernel_approximation.RBFSampler" title="sklearn.kernel_approximation.RBFSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kernel_approximation.RBFSampler</span></code></a>([gamma, …])</p></td>
<td><p>Approximates feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.kernel_approximation.SkewedChi2Sampler.html#sklearn.kernel_approximation.SkewedChi2Sampler" title="sklearn.kernel_approximation.SkewedChi2Sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kernel_approximation.SkewedChi2Sampler</span></code></a>([…])</p></td>
<td><p>Approximates feature map of the “skewed chi-squared” kernel by Monte Carlo approximation of its Fourier transform.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.kernel_ridge">
<span id="sklearn-kernel-ridge-kernel-ridge-regression"></span><span id="kernel-ridge-ref"></span><h2><a class="reference internal" href="#module-sklearn.kernel_ridge" title="sklearn.kernel_ridge"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_ridge</span></code></a> Kernel Ridge Regression<a class="headerlink" href="#module-sklearn.kernel_ridge" title="Permalink to this headline">¶</a></h2>
<p>Module <a class="reference internal" href="#module-sklearn.kernel_ridge" title="sklearn.kernel_ridge"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_ridge</span></code></a> implements kernel ridge regression.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="kernel_ridge.html#kernel-ridge"><span class="std std-ref">Kernel ridge regression</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.kernel_ridge.KernelRidge.html#sklearn.kernel_ridge.KernelRidge" title="sklearn.kernel_ridge.KernelRidge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kernel_ridge.KernelRidge</span></code></a>([alpha, kernel, …])</p></td>
<td><p>Kernel ridge regression.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.linear_model">
<span id="sklearn-linear-model-linear-models"></span><span id="linear-model-ref"></span><h2><a class="reference internal" href="#module-sklearn.linear_model" title="sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code></a>: Linear Models<a class="headerlink" href="#module-sklearn.linear_model" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.linear_model" title="sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code></a> module implements a variety of linear models.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="linear_model.html#linear-model"><span class="std std-ref">Linear Models</span></a> section for further details.</p>
<p>The following subsections are only rough guidelines: the same estimator can
fall into multiple categories, depending on its parameters.</p>
<div class="section" id="linear-classifiers">
<h3>Linear classifiers<a class="headerlink" href="#linear-classifiers" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
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<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.LogisticRegression</span></code></a>([penalty, …])</p></td>
<td><p>Logistic Regression (aka logit, MaxEnt) classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.LogisticRegressionCV</span></code></a>([Cs, …])</p></td>
<td><p>Logistic Regression CV (aka logit, MaxEnt) classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model.PassiveAggressiveClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.PassiveAggressiveClassifier</span></code></a>([…])</p></td>
<td><p>Passive Aggressive Classifier</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron" title="sklearn.linear_model.Perceptron"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.Perceptron</span></code></a>([penalty, alpha, …])</p></td>
<td><p>Read more in the <a class="reference internal" href="linear_model.html#perceptron"><span class="std std-ref">User Guide</span></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.RidgeClassifier.html#sklearn.linear_model.RidgeClassifier" title="sklearn.linear_model.RidgeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.RidgeClassifier</span></code></a>([alpha, …])</p></td>
<td><p>Classifier using Ridge regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.RidgeClassifierCV.html#sklearn.linear_model.RidgeClassifierCV" title="sklearn.linear_model.RidgeClassifierCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.RidgeClassifierCV</span></code></a>([alphas, …])</p></td>
<td><p>Ridge classifier with built-in cross-validation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.SGDClassifier</span></code></a>([loss, penalty, …])</p></td>
<td><p>Linear classifiers (SVM, logistic regression, a.o.) with SGD training.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="classical-linear-regressors">
<h3>Classical linear regressors<a class="headerlink" href="#classical-linear-regressors" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.LinearRegression</span></code></a>([…])</p></td>
<td><p>Ordinary least squares Linear Regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.Ridge</span></code></a>([alpha, fit_intercept, …])</p></td>
<td><p>Linear least squares with l2 regularization.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV" title="sklearn.linear_model.RidgeCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.RidgeCV</span></code></a>([alphas, …])</p></td>
<td><p>Ridge regression with built-in cross-validation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.SGDRegressor</span></code></a>([loss, penalty, …])</p></td>
<td><p>Linear model fitted by minimizing a regularized empirical loss with SGD</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="regressors-with-variable-selection">
<h3>Regressors with variable selection<a class="headerlink" href="#regressors-with-variable-selection" title="Permalink to this headline">¶</a></h3>
<p>The following estimators have built-in variable selection fitting
procedures, but any estimator using a L1 or elastic-net penalty also
performs variable selection: typically <a class="reference internal" href="generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">SGDRegressor</span></code></a>
or <a class="reference internal" href="generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">SGDClassifier</span></code></a> with an appropriate penalty.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet" title="sklearn.linear_model.ElasticNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.ElasticNet</span></code></a>([alpha, l1_ratio, …])</p></td>
<td><p>Linear regression with combined L1 and L2 priors as regularizer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model.ElasticNetCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.ElasticNetCV</span></code></a>([l1_ratio, eps, …])</p></td>
<td><p>Elastic Net model with iterative fitting along a regularization path.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.Lars.html#sklearn.linear_model.Lars" title="sklearn.linear_model.Lars"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.Lars</span></code></a>([fit_intercept, verbose, …])</p></td>
<td><p>Least Angle Regression model a.k.a.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.LarsCV.html#sklearn.linear_model.LarsCV" title="sklearn.linear_model.LarsCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.LarsCV</span></code></a>([fit_intercept, …])</p></td>
<td><p>Cross-validated Least Angle Regression model.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso" title="sklearn.linear_model.Lasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.Lasso</span></code></a>([alpha, fit_intercept, …])</p></td>
<td><p>Linear Model trained with L1 prior as regularizer (aka the Lasso)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV" title="sklearn.linear_model.LassoCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.LassoCV</span></code></a>([eps, n_alphas, …])</p></td>
<td><p>Lasso linear model with iterative fitting along a regularization path.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.LassoLars.html#sklearn.linear_model.LassoLars" title="sklearn.linear_model.LassoLars"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.LassoLars</span></code></a>([alpha, …])</p></td>
<td><p>Lasso model fit with Least Angle Regression a.k.a.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.LassoLarsCV.html#sklearn.linear_model.LassoLarsCV" title="sklearn.linear_model.LassoLarsCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.LassoLarsCV</span></code></a>([fit_intercept, …])</p></td>
<td><p>Cross-validated Lasso, using the LARS algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.LassoLarsIC.html#sklearn.linear_model.LassoLarsIC" title="sklearn.linear_model.LassoLarsIC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.LassoLarsIC</span></code></a>([criterion, …])</p></td>
<td><p>Lasso model fit with Lars using BIC or AIC for model selection</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.OrthogonalMatchingPursuit.html#sklearn.linear_model.OrthogonalMatchingPursuit" title="sklearn.linear_model.OrthogonalMatchingPursuit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.OrthogonalMatchingPursuit</span></code></a>([…])</p></td>
<td><p>Orthogonal Matching Pursuit model (OMP)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html#sklearn.linear_model.OrthogonalMatchingPursuitCV" title="sklearn.linear_model.OrthogonalMatchingPursuitCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.OrthogonalMatchingPursuitCV</span></code></a>([…])</p></td>
<td><p>Cross-validated Orthogonal Matching Pursuit model (OMP).</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="bayesian-regressors">
<h3>Bayesian regressors<a class="headerlink" href="#bayesian-regressors" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.ARDRegression.html#sklearn.linear_model.ARDRegression" title="sklearn.linear_model.ARDRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.ARDRegression</span></code></a>([n_iter, tol, …])</p></td>
<td><p>Bayesian ARD regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.BayesianRidge.html#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model.BayesianRidge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.BayesianRidge</span></code></a>([n_iter, tol, …])</p></td>
<td><p>Bayesian ridge regression.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="multi-task-linear-regressors-with-variable-selection">
<h3>Multi-task linear regressors with variable selection<a class="headerlink" href="#multi-task-linear-regressors-with-variable-selection" title="Permalink to this headline">¶</a></h3>
<p>These estimators fit multiple regression problems (or tasks) jointly, while
inducing sparse coefficients. While the inferred coefficients may differ
between the tasks, they are constrained to agree on the features that are
selected (non-zero coefficients).</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.MultiTaskElasticNet.html#sklearn.linear_model.MultiTaskElasticNet" title="sklearn.linear_model.MultiTaskElasticNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.MultiTaskElasticNet</span></code></a>([alpha, …])</p></td>
<td><p>Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.MultiTaskElasticNetCV.html#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model.MultiTaskElasticNetCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.MultiTaskElasticNetCV</span></code></a>([…])</p></td>
<td><p>Multi-task L1/L2 ElasticNet with built-in cross-validation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.MultiTaskLasso.html#sklearn.linear_model.MultiTaskLasso" title="sklearn.linear_model.MultiTaskLasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.MultiTaskLasso</span></code></a>([alpha, …])</p></td>
<td><p>Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.MultiTaskLassoCV.html#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model.MultiTaskLassoCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.MultiTaskLassoCV</span></code></a>([eps, …])</p></td>
<td><p>Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="outlier-robust-regressors">
<h3>Outlier-robust regressors<a class="headerlink" href="#outlier-robust-regressors" title="Permalink to this headline">¶</a></h3>
<p>Any estimator using the Huber loss would also be robust to outliers, e.g.
<a class="reference internal" href="generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">SGDRegressor</span></code></a> with <code class="docutils literal notranslate"><span class="pre">loss='huber'</span></code>.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.HuberRegressor.html#sklearn.linear_model.HuberRegressor" title="sklearn.linear_model.HuberRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.HuberRegressor</span></code></a>([epsilon, …])</p></td>
<td><p>Linear regression model that is robust to outliers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.RANSACRegressor.html#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model.RANSACRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.RANSACRegressor</span></code></a>([…])</p></td>
<td><p>RANSAC (RANdom SAmple Consensus) algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.TheilSenRegressor.html#sklearn.linear_model.TheilSenRegressor" title="sklearn.linear_model.TheilSenRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.TheilSenRegressor</span></code></a>([…])</p></td>
<td><p>Theil-Sen Estimator: robust multivariate regression model.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="miscellaneous">
<h3>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.PassiveAggressiveRegressor.html#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model.PassiveAggressiveRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.PassiveAggressiveRegressor</span></code></a>([C, …])</p></td>
<td><p>Passive Aggressive Regressor</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.enet_path.html#sklearn.linear_model.enet_path" title="sklearn.linear_model.enet_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.enet_path</span></code></a>(X, y[, l1_ratio, …])</p></td>
<td><p>Compute elastic net path with coordinate descent.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.lars_path.html#sklearn.linear_model.lars_path" title="sklearn.linear_model.lars_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.lars_path</span></code></a>(X, y[, Xy, Gram, …])</p></td>
<td><p>Compute Least Angle Regression or Lasso path using LARS algorithm [1]</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.lars_path_gram.html#sklearn.linear_model.lars_path_gram" title="sklearn.linear_model.lars_path_gram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.lars_path_gram</span></code></a>(Xy, Gram, n_samples)</p></td>
<td><p>lars_path in the sufficient stats mode [1]</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.lasso_path.html#sklearn.linear_model.lasso_path" title="sklearn.linear_model.lasso_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.lasso_path</span></code></a>(X, y[, eps, …])</p></td>
<td><p>Compute Lasso path with coordinate descent</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.orthogonal_mp.html#sklearn.linear_model.orthogonal_mp" title="sklearn.linear_model.orthogonal_mp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.orthogonal_mp</span></code></a>(X, y[, …])</p></td>
<td><p>Orthogonal Matching Pursuit (OMP)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.orthogonal_mp_gram.html#sklearn.linear_model.orthogonal_mp_gram" title="sklearn.linear_model.orthogonal_mp_gram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.orthogonal_mp_gram</span></code></a>(Gram, Xy[, …])</p></td>
<td><p>Gram Orthogonal Matching Pursuit (OMP)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.linear_model.ridge_regression.html#sklearn.linear_model.ridge_regression" title="sklearn.linear_model.ridge_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.ridge_regression</span></code></a>(X, y, alpha[, …])</p></td>
<td><p>Solve the ridge equation by the method of normal equations.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.manifold">
<span id="sklearn-manifold-manifold-learning"></span><span id="manifold-ref"></span><h2><a class="reference internal" href="#module-sklearn.manifold" title="sklearn.manifold"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.manifold</span></code></a>: Manifold Learning<a class="headerlink" href="#module-sklearn.manifold" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.manifold" title="sklearn.manifold"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.manifold</span></code></a> module implements data embedding techniques.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="manifold.html#manifold"><span class="std std-ref">Manifold learning</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.manifold.Isomap.html#sklearn.manifold.Isomap" title="sklearn.manifold.Isomap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.Isomap</span></code></a>([n_neighbors, n_components, …])</p></td>
<td><p>Isomap Embedding</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.manifold.LocallyLinearEmbedding.html#sklearn.manifold.LocallyLinearEmbedding" title="sklearn.manifold.LocallyLinearEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.LocallyLinearEmbedding</span></code></a>([…])</p></td>
<td><p>Locally Linear Embedding</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.MDS</span></code></a>([n_components, metric, n_init, …])</p></td>
<td><p>Multidimensional scaling</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.manifold.SpectralEmbedding.html#sklearn.manifold.SpectralEmbedding" title="sklearn.manifold.SpectralEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.SpectralEmbedding</span></code></a>([n_components, …])</p></td>
<td><p>Spectral embedding for non-linear dimensionality reduction.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE" title="sklearn.manifold.TSNE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.TSNE</span></code></a>([n_components, perplexity, …])</p></td>
<td><p>t-distributed Stochastic Neighbor Embedding.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.manifold.locally_linear_embedding.html#sklearn.manifold.locally_linear_embedding" title="sklearn.manifold.locally_linear_embedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.locally_linear_embedding</span></code></a>(X, …[, …])</p></td>
<td><p>Perform a Locally Linear Embedding analysis on the data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.manifold.smacof.html#sklearn.manifold.smacof" title="sklearn.manifold.smacof"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.smacof</span></code></a>(dissimilarities[, metric, …])</p></td>
<td><p>Computes multidimensional scaling using the SMACOF algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.manifold.spectral_embedding.html#sklearn.manifold.spectral_embedding" title="sklearn.manifold.spectral_embedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.spectral_embedding</span></code></a>(adjacency[, …])</p></td>
<td><p>Project the sample on the first eigenvectors of the graph Laplacian.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.manifold.trustworthiness.html#sklearn.manifold.trustworthiness" title="sklearn.manifold.trustworthiness"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manifold.trustworthiness</span></code></a>(X, X_embedded[, …])</p></td>
<td><p>Expresses to what extent the local structure is retained.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="sklearn-metrics-metrics">
<span id="metrics-ref"></span><h2><a class="reference internal" href="#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code></a>: Metrics<a class="headerlink" href="#sklearn-metrics-metrics" title="Permalink to this headline">¶</a></h2>
<p>See the <a class="reference internal" href="model_evaluation.html#model-evaluation"><span class="std std-ref">Metrics and scoring: quantifying the quality of predictions</span></a> section and the <a class="reference internal" href="metrics.html#metrics"><span class="std std-ref">Pairwise metrics, Affinities and Kernels</span></a> section of the
user guide for further details.</p>
<span class="target" id="module-sklearn.metrics"></span><p>The <a class="reference internal" href="#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code></a> module includes score functions, performance metrics
and pairwise metrics and distance computations.</p>
<div class="section" id="model-selection-interface">
<h3>Model Selection Interface<a class="headerlink" href="#model-selection-interface" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference internal" href="model_evaluation.html#scoring-parameter"><span class="std std-ref">The scoring parameter: defining model evaluation rules</span></a> section of the user guide for further
details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.check_scoring.html#sklearn.metrics.check_scoring" title="sklearn.metrics.check_scoring"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.check_scoring</span></code></a>(estimator[, scoring, …])</p></td>
<td><p>Determine scorer from user options.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.get_scorer.html#sklearn.metrics.get_scorer" title="sklearn.metrics.get_scorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.get_scorer</span></code></a>(scoring)</p></td>
<td><p>Get a scorer from string.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.make_scorer</span></code></a>(score_func[, …])</p></td>
<td><p>Make a scorer from a performance metric or loss function.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="classification-metrics">
<h3>Classification metrics<a class="headerlink" href="#classification-metrics" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference internal" href="model_evaluation.html#classification-metrics"><span class="std std-ref">Classification metrics</span></a> section of the user guide for further
details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" title="sklearn.metrics.accuracy_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.accuracy_score</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Accuracy classification score.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.auc.html#sklearn.metrics.auc" title="sklearn.metrics.auc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.auc</span></code></a>(x, y)</p></td>
<td><p>Compute Area Under the Curve (AUC) using the trapezoidal rule</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score" title="sklearn.metrics.average_precision_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.average_precision_score</span></code></a>(y_true, y_score)</p></td>
<td><p>Compute average precision (AP) from prediction scores</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.balanced_accuracy_score</span></code></a>(y_true, y_pred)</p></td>
<td><p>Compute the balanced accuracy</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.brier_score_loss.html#sklearn.metrics.brier_score_loss" title="sklearn.metrics.brier_score_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.brier_score_loss</span></code></a>(y_true, y_prob[, …])</p></td>
<td><p>Compute the Brier score.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.classification_report</span></code></a>(y_true, y_pred)</p></td>
<td><p>Build a text report showing the main classification metrics</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.cohen_kappa_score.html#sklearn.metrics.cohen_kappa_score" title="sklearn.metrics.cohen_kappa_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.cohen_kappa_score</span></code></a>(y1, y2[, labels, …])</p></td>
<td><p>Cohen’s kappa: a statistic that measures inter-annotator agreement.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.confusion_matrix</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Compute confusion matrix to evaluate the accuracy of a classification.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.dcg_score.html#sklearn.metrics.dcg_score" title="sklearn.metrics.dcg_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.dcg_score</span></code></a>(y_true, y_score[, k, …])</p></td>
<td><p>Compute Discounted Cumulative Gain.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score" title="sklearn.metrics.f1_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.f1_score</span></code></a>(y_true, y_pred[, labels, …])</p></td>
<td><p>Compute the F1 score, also known as balanced F-score or F-measure</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.fbeta_score.html#sklearn.metrics.fbeta_score" title="sklearn.metrics.fbeta_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.fbeta_score</span></code></a>(y_true, y_pred, beta[, …])</p></td>
<td><p>Compute the F-beta score</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.hamming_loss.html#sklearn.metrics.hamming_loss" title="sklearn.metrics.hamming_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.hamming_loss</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Compute the average Hamming loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.hinge_loss.html#sklearn.metrics.hinge_loss" title="sklearn.metrics.hinge_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.hinge_loss</span></code></a>(y_true, pred_decision[, …])</p></td>
<td><p>Average hinge loss (non-regularized)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.jaccard_score.html#sklearn.metrics.jaccard_score" title="sklearn.metrics.jaccard_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.jaccard_score</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Jaccard similarity coefficient score</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.log_loss.html#sklearn.metrics.log_loss" title="sklearn.metrics.log_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.log_loss</span></code></a>(y_true, y_pred[, eps, …])</p></td>
<td><p>Log loss, aka logistic loss or cross-entropy loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.matthews_corrcoef.html#sklearn.metrics.matthews_corrcoef" title="sklearn.metrics.matthews_corrcoef"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.matthews_corrcoef</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Compute the Matthews correlation coefficient (MCC)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.multilabel_confusion_matrix.html#sklearn.metrics.multilabel_confusion_matrix" title="sklearn.metrics.multilabel_confusion_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.multilabel_confusion_matrix</span></code></a>(y_true, …)</p></td>
<td><p>Compute a confusion matrix for each class or sample</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.ndcg_score.html#sklearn.metrics.ndcg_score" title="sklearn.metrics.ndcg_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.ndcg_score</span></code></a>(y_true, y_score[, k, …])</p></td>
<td><p>Compute Normalized Discounted Cumulative Gain.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve" title="sklearn.metrics.precision_recall_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.precision_recall_curve</span></code></a>(y_true, …)</p></td>
<td><p>Compute precision-recall pairs for different probability thresholds</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.precision_recall_fscore_support.html#sklearn.metrics.precision_recall_fscore_support" title="sklearn.metrics.precision_recall_fscore_support"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.precision_recall_fscore_support</span></code></a>(…)</p></td>
<td><p>Compute precision, recall, F-measure and support for each class</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score" title="sklearn.metrics.precision_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.precision_score</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Compute the precision</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score" title="sklearn.metrics.recall_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.recall_score</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Compute the recall</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.roc_auc_score</span></code></a>(y_true, y_score[, …])</p></td>
<td><p>Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve" title="sklearn.metrics.roc_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.roc_curve</span></code></a>(y_true, y_score[, …])</p></td>
<td><p>Compute Receiver operating characteristic (ROC)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.zero_one_loss.html#sklearn.metrics.zero_one_loss" title="sklearn.metrics.zero_one_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.zero_one_loss</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Zero-one classification loss.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="regression-metrics">
<h3>Regression metrics<a class="headerlink" href="#regression-metrics" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference internal" href="model_evaluation.html#regression-metrics"><span class="std std-ref">Regression metrics</span></a> section of the user guide for further
details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.explained_variance_score.html#sklearn.metrics.explained_variance_score" title="sklearn.metrics.explained_variance_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.explained_variance_score</span></code></a>(y_true, y_pred)</p></td>
<td><p>Explained variance regression score function</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.max_error.html#sklearn.metrics.max_error" title="sklearn.metrics.max_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.max_error</span></code></a>(y_true, y_pred)</p></td>
<td><p>max_error metric calculates the maximum residual error.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error" title="sklearn.metrics.mean_absolute_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.mean_absolute_error</span></code></a>(y_true, y_pred)</p></td>
<td><p>Mean absolute error regression loss</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" title="sklearn.metrics.mean_squared_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.mean_squared_error</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>Mean squared error regression loss</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.mean_squared_log_error.html#sklearn.metrics.mean_squared_log_error" title="sklearn.metrics.mean_squared_log_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.mean_squared_log_error</span></code></a>(y_true, y_pred)</p></td>
<td><p>Mean squared logarithmic error regression loss</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.median_absolute_error.html#sklearn.metrics.median_absolute_error" title="sklearn.metrics.median_absolute_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.median_absolute_error</span></code></a>(y_true, y_pred)</p></td>
<td><p>Median absolute error regression loss</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.r2_score</span></code></a>(y_true, y_pred[, …])</p></td>
<td><p>R^2 (coefficient of determination) regression score function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.mean_poisson_deviance.html#sklearn.metrics.mean_poisson_deviance" title="sklearn.metrics.mean_poisson_deviance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.mean_poisson_deviance</span></code></a>(y_true, y_pred)</p></td>
<td><p>Mean Poisson deviance regression loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.mean_gamma_deviance.html#sklearn.metrics.mean_gamma_deviance" title="sklearn.metrics.mean_gamma_deviance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.mean_gamma_deviance</span></code></a>(y_true, y_pred)</p></td>
<td><p>Mean Gamma deviance regression loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.mean_tweedie_deviance.html#sklearn.metrics.mean_tweedie_deviance" title="sklearn.metrics.mean_tweedie_deviance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.mean_tweedie_deviance</span></code></a>(y_true, y_pred)</p></td>
<td><p>Mean Tweedie deviance regression loss.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="multilabel-ranking-metrics">
<h3>Multilabel ranking metrics<a class="headerlink" href="#multilabel-ranking-metrics" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference internal" href="model_evaluation.html#multilabel-ranking-metrics"><span class="std std-ref">Multilabel ranking metrics</span></a> section of the user guide for further
details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.coverage_error.html#sklearn.metrics.coverage_error" title="sklearn.metrics.coverage_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.coverage_error</span></code></a>(y_true, y_score[, …])</p></td>
<td><p>Coverage error measure</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.label_ranking_average_precision_score.html#sklearn.metrics.label_ranking_average_precision_score" title="sklearn.metrics.label_ranking_average_precision_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.label_ranking_average_precision_score</span></code></a>(…)</p></td>
<td><p>Compute ranking-based average precision</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.label_ranking_loss.html#sklearn.metrics.label_ranking_loss" title="sklearn.metrics.label_ranking_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.label_ranking_loss</span></code></a>(y_true, y_score)</p></td>
<td><p>Compute Ranking loss measure</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="clustering-metrics">
<h3>Clustering metrics<a class="headerlink" href="#clustering-metrics" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference internal" href="clustering.html#clustering-evaluation"><span class="std std-ref">Clustering performance evaluation</span></a> section of the user guide for further
details.</p>
<span class="target" id="module-sklearn.metrics.cluster"></span><p>The <a class="reference internal" href="#module-sklearn.metrics.cluster" title="sklearn.metrics.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics.cluster</span></code></a> submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:</p>
<ul class="simple">
<li><p>supervised, which uses a ground truth class values for each sample.</p></li>
<li><p>unsupervised, which does not and measures the ‘quality’ of the model itself.</p></li>
</ul>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score" title="sklearn.metrics.adjusted_mutual_info_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.adjusted_mutual_info_score</span></code></a>(…[, …])</p></td>
<td><p>Adjusted Mutual Information between two clusterings.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.adjusted_rand_score.html#sklearn.metrics.adjusted_rand_score" title="sklearn.metrics.adjusted_rand_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.adjusted_rand_score</span></code></a>(labels_true, …)</p></td>
<td><p>Rand index adjusted for chance.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.calinski_harabasz_score.html#sklearn.metrics.calinski_harabasz_score" title="sklearn.metrics.calinski_harabasz_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.calinski_harabasz_score</span></code></a>(X, labels)</p></td>
<td><p>Compute the Calinski and Harabasz score.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.davies_bouldin_score.html#sklearn.metrics.davies_bouldin_score" title="sklearn.metrics.davies_bouldin_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.davies_bouldin_score</span></code></a>(X, labels)</p></td>
<td><p>Computes the Davies-Bouldin score.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.completeness_score.html#sklearn.metrics.completeness_score" title="sklearn.metrics.completeness_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.completeness_score</span></code></a>(labels_true, …)</p></td>
<td><p>Completeness metric of a cluster labeling given a ground truth.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.cluster.contingency_matrix.html#sklearn.metrics.cluster.contingency_matrix" title="sklearn.metrics.cluster.contingency_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.cluster.contingency_matrix</span></code></a>(…[, …])</p></td>
<td><p>Build a contingency matrix describing the relationship between labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.fowlkes_mallows_score.html#sklearn.metrics.fowlkes_mallows_score" title="sklearn.metrics.fowlkes_mallows_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.fowlkes_mallows_score</span></code></a>(labels_true, …)</p></td>
<td><p>Measure the similarity of two clusterings of a set of points.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.homogeneity_completeness_v_measure.html#sklearn.metrics.homogeneity_completeness_v_measure" title="sklearn.metrics.homogeneity_completeness_v_measure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.homogeneity_completeness_v_measure</span></code></a>(…)</p></td>
<td><p>Compute the homogeneity and completeness and V-Measure scores at once.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.homogeneity_score.html#sklearn.metrics.homogeneity_score" title="sklearn.metrics.homogeneity_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.homogeneity_score</span></code></a>(labels_true, …)</p></td>
<td><p>Homogeneity metric of a cluster labeling given a ground truth.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.mutual_info_score.html#sklearn.metrics.mutual_info_score" title="sklearn.metrics.mutual_info_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.mutual_info_score</span></code></a>(labels_true, …)</p></td>
<td><p>Mutual Information between two clusterings.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.normalized_mutual_info_score.html#sklearn.metrics.normalized_mutual_info_score" title="sklearn.metrics.normalized_mutual_info_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.normalized_mutual_info_score</span></code></a>(…[, …])</p></td>
<td><p>Normalized Mutual Information between two clusterings.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score" title="sklearn.metrics.silhouette_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.silhouette_score</span></code></a>(X, labels[, …])</p></td>
<td><p>Compute the mean Silhouette Coefficient of all samples.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.silhouette_samples.html#sklearn.metrics.silhouette_samples" title="sklearn.metrics.silhouette_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.silhouette_samples</span></code></a>(X, labels[, metric])</p></td>
<td><p>Compute the Silhouette Coefficient for each sample.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="sklearn.metrics.v_measure_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.v_measure_score</span></code></a>(labels_true, labels_pred)</p></td>
<td><p>V-measure cluster labeling given a ground truth.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="biclustering-metrics">
<h3>Biclustering metrics<a class="headerlink" href="#biclustering-metrics" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference internal" href="biclustering.html#biclustering-evaluation"><span class="std std-ref">Biclustering evaluation</span></a> section of the user guide for
further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.consensus_score.html#sklearn.metrics.consensus_score" title="sklearn.metrics.consensus_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.consensus_score</span></code></a>(a, b[, similarity])</p></td>
<td><p>The similarity of two sets of biclusters.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="pairwise-metrics">
<h3>Pairwise metrics<a class="headerlink" href="#pairwise-metrics" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference internal" href="metrics.html#metrics"><span class="std std-ref">Pairwise metrics, Affinities and Kernels</span></a> section of the user guide for further details.</p>
<span class="target" id="module-sklearn.metrics.pairwise"></span><table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.additive_chi2_kernel.html#sklearn.metrics.pairwise.additive_chi2_kernel" title="sklearn.metrics.pairwise.additive_chi2_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.additive_chi2_kernel</span></code></a>(X[, Y])</p></td>
<td><p>Computes the additive chi-squared kernel between observations in X and Y</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.chi2_kernel.html#sklearn.metrics.pairwise.chi2_kernel" title="sklearn.metrics.pairwise.chi2_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.chi2_kernel</span></code></a>(X[, Y, gamma])</p></td>
<td><p>Computes the exponential chi-squared kernel X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.cosine_similarity.html#sklearn.metrics.pairwise.cosine_similarity" title="sklearn.metrics.pairwise.cosine_similarity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.cosine_similarity</span></code></a>(X[, Y, …])</p></td>
<td><p>Compute cosine similarity between samples in X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.cosine_distances.html#sklearn.metrics.pairwise.cosine_distances" title="sklearn.metrics.pairwise.cosine_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.cosine_distances</span></code></a>(X[, Y])</p></td>
<td><p>Compute cosine distance between samples in X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.distance_metrics.html#sklearn.metrics.pairwise.distance_metrics" title="sklearn.metrics.pairwise.distance_metrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.distance_metrics</span></code></a>()</p></td>
<td><p>Valid metrics for pairwise_distances.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.euclidean_distances.html#sklearn.metrics.pairwise.euclidean_distances" title="sklearn.metrics.pairwise.euclidean_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.euclidean_distances</span></code></a>(X[, Y, …])</p></td>
<td><p>Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.haversine_distances.html#sklearn.metrics.pairwise.haversine_distances" title="sklearn.metrics.pairwise.haversine_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.haversine_distances</span></code></a>(X[, Y])</p></td>
<td><p>Compute the Haversine distance between samples in X and Y</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.kernel_metrics.html#sklearn.metrics.pairwise.kernel_metrics" title="sklearn.metrics.pairwise.kernel_metrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.kernel_metrics</span></code></a>()</p></td>
<td><p>Valid metrics for pairwise_kernels</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.laplacian_kernel.html#sklearn.metrics.pairwise.laplacian_kernel" title="sklearn.metrics.pairwise.laplacian_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.laplacian_kernel</span></code></a>(X[, Y, gamma])</p></td>
<td><p>Compute the laplacian kernel between X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.linear_kernel.html#sklearn.metrics.pairwise.linear_kernel" title="sklearn.metrics.pairwise.linear_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.linear_kernel</span></code></a>(X[, Y, …])</p></td>
<td><p>Compute the linear kernel between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.manhattan_distances.html#sklearn.metrics.pairwise.manhattan_distances" title="sklearn.metrics.pairwise.manhattan_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.manhattan_distances</span></code></a>(X[, Y, …])</p></td>
<td><p>Compute the L1 distances between the vectors in X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.nan_euclidean_distances.html#sklearn.metrics.pairwise.nan_euclidean_distances" title="sklearn.metrics.pairwise.nan_euclidean_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.nan_euclidean_distances</span></code></a>(X)</p></td>
<td><p>Calculate the euclidean distances in the presence of missing values.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.pairwise_kernels.html#sklearn.metrics.pairwise.pairwise_kernels" title="sklearn.metrics.pairwise.pairwise_kernels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.pairwise_kernels</span></code></a>(X[, Y, …])</p></td>
<td><p>Compute the kernel between arrays X and optional array Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.polynomial_kernel.html#sklearn.metrics.pairwise.polynomial_kernel" title="sklearn.metrics.pairwise.polynomial_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.polynomial_kernel</span></code></a>(X[, Y, …])</p></td>
<td><p>Compute the polynomial kernel between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.rbf_kernel.html#sklearn.metrics.pairwise.rbf_kernel" title="sklearn.metrics.pairwise.rbf_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.rbf_kernel</span></code></a>(X[, Y, gamma])</p></td>
<td><p>Compute the rbf (gaussian) kernel between X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.sigmoid_kernel.html#sklearn.metrics.pairwise.sigmoid_kernel" title="sklearn.metrics.pairwise.sigmoid_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.sigmoid_kernel</span></code></a>(X[, Y, …])</p></td>
<td><p>Compute the sigmoid kernel between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.paired_euclidean_distances.html#sklearn.metrics.pairwise.paired_euclidean_distances" title="sklearn.metrics.pairwise.paired_euclidean_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.paired_euclidean_distances</span></code></a>(X, Y)</p></td>
<td><p>Computes the paired euclidean distances between X and Y</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.paired_manhattan_distances.html#sklearn.metrics.pairwise.paired_manhattan_distances" title="sklearn.metrics.pairwise.paired_manhattan_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.paired_manhattan_distances</span></code></a>(X, Y)</p></td>
<td><p>Compute the L1 distances between the vectors in X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.paired_cosine_distances.html#sklearn.metrics.pairwise.paired_cosine_distances" title="sklearn.metrics.pairwise.paired_cosine_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.paired_cosine_distances</span></code></a>(X, Y)</p></td>
<td><p>Computes the paired cosine distances between X and Y</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.paired_distances.html#sklearn.metrics.pairwise.paired_distances" title="sklearn.metrics.pairwise.paired_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise.paired_distances</span></code></a>(X, Y[, metric])</p></td>
<td><p>Computes the paired distances between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances" title="sklearn.metrics.pairwise_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise_distances</span></code></a>(X[, Y, metric, …])</p></td>
<td><p>Compute the distance matrix from a vector array X and optional Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise_distances_argmin.html#sklearn.metrics.pairwise_distances_argmin" title="sklearn.metrics.pairwise_distances_argmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise_distances_argmin</span></code></a>(X, Y[, …])</p></td>
<td><p>Compute minimum distances between one point and a set of points.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise_distances_argmin_min.html#sklearn.metrics.pairwise_distances_argmin_min" title="sklearn.metrics.pairwise_distances_argmin_min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise_distances_argmin_min</span></code></a>(X, Y)</p></td>
<td><p>Compute minimum distances between one point and a set of points.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.pairwise_distances_chunked.html#sklearn.metrics.pairwise_distances_chunked" title="sklearn.metrics.pairwise_distances_chunked"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.pairwise_distances_chunked</span></code></a>(X[, Y, …])</p></td>
<td><p>Generate a distance matrix chunk by chunk with optional reduction</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="id3">
<h3>Plotting<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference internal" href="../visualizations.html#visualizations"><span class="std std-ref">Visualizations</span></a> section of the user guide for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.plot_confusion_matrix.html#sklearn.metrics.plot_confusion_matrix" title="sklearn.metrics.plot_confusion_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.plot_confusion_matrix</span></code></a>(estimator, X, …)</p></td>
<td><p>Plot Confusion Matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.plot_precision_recall_curve.html#sklearn.metrics.plot_precision_recall_curve" title="sklearn.metrics.plot_precision_recall_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.plot_precision_recall_curve</span></code></a>(…[, …])</p></td>
<td><p>Plot Precision Recall Curve for binary classifers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.plot_roc_curve.html#sklearn.metrics.plot_roc_curve" title="sklearn.metrics.plot_roc_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.plot_roc_curve</span></code></a>(estimator, X, y[, …])</p></td>
<td><p>Plot Receiver operating characteristic (ROC) curve.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay" title="sklearn.metrics.ConfusionMatrixDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.ConfusionMatrixDisplay</span></code></a>(…)</p></td>
<td><p>Confusion Matrix visualization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.PrecisionRecallDisplay.html#sklearn.metrics.PrecisionRecallDisplay" title="sklearn.metrics.PrecisionRecallDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.PrecisionRecallDisplay</span></code></a>(precision, …)</p></td>
<td><p>Precision Recall visualization.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay" title="sklearn.metrics.RocCurveDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.RocCurveDisplay</span></code></a>(fpr, tpr, roc_auc, …)</p></td>
<td><p>ROC Curve visualization.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.mixture">
<span id="sklearn-mixture-gaussian-mixture-models"></span><span id="mixture-ref"></span><h2><a class="reference internal" href="#module-sklearn.mixture" title="sklearn.mixture"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.mixture</span></code></a>: Gaussian Mixture Models<a class="headerlink" href="#module-sklearn.mixture" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.mixture" title="sklearn.mixture"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.mixture</span></code></a> module implements mixture modeling algorithms.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="mixture.html#mixture"><span class="std std-ref">Gaussian mixture models</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.mixture.BayesianGaussianMixture.html#sklearn.mixture.BayesianGaussianMixture" title="sklearn.mixture.BayesianGaussianMixture"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mixture.BayesianGaussianMixture</span></code></a>([…])</p></td>
<td><p>Variational Bayesian estimation of a Gaussian mixture.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture" title="sklearn.mixture.GaussianMixture"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mixture.GaussianMixture</span></code></a>([n_components, …])</p></td>
<td><p>Gaussian Mixture.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.model_selection">
<span id="sklearn-model-selection-model-selection"></span><span id="modelselection-ref"></span><h2><a class="reference internal" href="#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a>: Model Selection<a class="headerlink" href="#module-sklearn.model_selection" title="Permalink to this headline">¶</a></h2>
<p><strong>User guide:</strong> See the <a class="reference internal" href="cross_validation.html#cross-validation"><span class="std std-ref">Cross-validation: evaluating estimator performance</span></a>, <a class="reference internal" href="grid_search.html#grid-search"><span class="std std-ref">Tuning the hyper-parameters of an estimator</span></a> and
<a class="reference internal" href="learning_curve.html#learning-curve"><span class="std std-ref">Learning curve</span></a> sections for further details.</p>
<div class="section" id="splitter-classes">
<h3>Splitter Classes<a class="headerlink" href="#splitter-classes" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.GroupKFold</span></code></a>([n_splits])</p></td>
<td><p>K-fold iterator variant with non-overlapping groups.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.GroupShuffleSplit.html#sklearn.model_selection.GroupShuffleSplit" title="sklearn.model_selection.GroupShuffleSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.GroupShuffleSplit</span></code></a>([…])</p></td>
<td><p>Shuffle-Group(s)-Out cross-validation iterator</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.KFold</span></code></a>([n_splits, shuffle, …])</p></td>
<td><p>K-Folds cross-validator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.LeaveOneGroupOut.html#sklearn.model_selection.LeaveOneGroupOut" title="sklearn.model_selection.LeaveOneGroupOut"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.LeaveOneGroupOut</span></code></a></p></td>
<td><p>Leave One Group Out cross-validator</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.LeavePGroupsOut.html#sklearn.model_selection.LeavePGroupsOut" title="sklearn.model_selection.LeavePGroupsOut"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.LeavePGroupsOut</span></code></a>(n_groups)</p></td>
<td><p>Leave P Group(s) Out cross-validator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.LeaveOneOut.html#sklearn.model_selection.LeaveOneOut" title="sklearn.model_selection.LeaveOneOut"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.LeaveOneOut</span></code></a></p></td>
<td><p>Leave-One-Out cross-validator</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.LeavePOut.html#sklearn.model_selection.LeavePOut" title="sklearn.model_selection.LeavePOut"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.LeavePOut</span></code></a>(p)</p></td>
<td><p>Leave-P-Out cross-validator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.PredefinedSplit.html#sklearn.model_selection.PredefinedSplit" title="sklearn.model_selection.PredefinedSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.PredefinedSplit</span></code></a>(test_fold)</p></td>
<td><p>Predefined split cross-validator</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.RepeatedKFold.html#sklearn.model_selection.RepeatedKFold" title="sklearn.model_selection.RepeatedKFold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.RepeatedKFold</span></code></a>([n_splits, …])</p></td>
<td><p>Repeated K-Fold cross validator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.RepeatedStratifiedKFold.html#sklearn.model_selection.RepeatedStratifiedKFold" title="sklearn.model_selection.RepeatedStratifiedKFold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.RepeatedStratifiedKFold</span></code></a>([…])</p></td>
<td><p>Repeated Stratified K-Fold cross validator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit" title="sklearn.model_selection.ShuffleSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.ShuffleSplit</span></code></a>([n_splits, …])</p></td>
<td><p>Random permutation cross-validator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.StratifiedKFold</span></code></a>([n_splits, …])</p></td>
<td><p>Stratified K-Folds cross-validator</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit" title="sklearn.model_selection.StratifiedShuffleSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.StratifiedShuffleSplit</span></code></a>([…])</p></td>
<td><p>Stratified ShuffleSplit cross-validator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.TimeSeriesSplit.html#sklearn.model_selection.TimeSeriesSplit" title="sklearn.model_selection.TimeSeriesSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.TimeSeriesSplit</span></code></a>([n_splits, …])</p></td>
<td><p>Time Series cross-validator</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="splitter-functions">
<h3>Splitter Functions<a class="headerlink" href="#splitter-functions" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.check_cv.html#sklearn.model_selection.check_cv" title="sklearn.model_selection.check_cv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.check_cv</span></code></a>([cv, y, classifier])</p></td>
<td><p>Input checker utility for building a cross-validator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.train_test_split</span></code></a>(\*arrays, …)</p></td>
<td><p>Split arrays or matrices into random train and test subsets</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="hyper-parameter-optimizers">
<h3>Hyper-parameter optimizers<a class="headerlink" href="#hyper-parameter-optimizers" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.GridSearchCV</span></code></a>(estimator, …)</p></td>
<td><p>Exhaustive search over specified parameter values for an estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.ParameterGrid.html#sklearn.model_selection.ParameterGrid" title="sklearn.model_selection.ParameterGrid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.ParameterGrid</span></code></a>(param_grid)</p></td>
<td><p>Grid of parameters with a discrete number of values for each.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.ParameterSampler.html#sklearn.model_selection.ParameterSampler" title="sklearn.model_selection.ParameterSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.ParameterSampler</span></code></a>(…[, …])</p></td>
<td><p>Generator on parameters sampled from given distributions.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.RandomizedSearchCV</span></code></a>(…[, …])</p></td>
<td><p>Randomized search on hyper parameters.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.fit_grid_point.html#sklearn.model_selection.fit_grid_point" title="sklearn.model_selection.fit_grid_point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.fit_grid_point</span></code></a>(X, y, …[, …])</p></td>
<td><p>Run fit on one set of parameters.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="model-validation">
<h3>Model validation<a class="headerlink" href="#model-validation" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.cross_validate</span></code></a>(estimator, X)</p></td>
<td><p>Evaluate metric(s) by cross-validation and also record fit/score times.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.cross_val_predict.html#sklearn.model_selection.cross_val_predict" title="sklearn.model_selection.cross_val_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.cross_val_predict</span></code></a>(estimator, X)</p></td>
<td><p>Generate cross-validated estimates for each input data point</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.cross_val_score</span></code></a>(estimator, X)</p></td>
<td><p>Evaluate a score by cross-validation</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.learning_curve.html#sklearn.model_selection.learning_curve" title="sklearn.model_selection.learning_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.learning_curve</span></code></a>(estimator, X, y)</p></td>
<td><p>Learning curve.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.model_selection.permutation_test_score.html#sklearn.model_selection.permutation_test_score" title="sklearn.model_selection.permutation_test_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.permutation_test_score</span></code></a>(…)</p></td>
<td><p>Evaluate the significance of a cross-validated score with permutations</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.model_selection.validation_curve.html#sklearn.model_selection.validation_curve" title="sklearn.model_selection.validation_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.validation_curve</span></code></a>(estimator, …)</p></td>
<td><p>Validation curve.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.multiclass">
<span id="sklearn-multiclass-multiclass-and-multilabel-classification"></span><span id="multiclass-ref"></span><h2><a class="reference internal" href="#module-sklearn.multiclass" title="sklearn.multiclass"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multiclass</span></code></a>: Multiclass and multilabel classification<a class="headerlink" href="#module-sklearn.multiclass" title="Permalink to this headline">¶</a></h2>
<div class="section" id="multiclass-and-multilabel-classification-strategies">
<h3>Multiclass and multilabel classification strategies<a class="headerlink" href="#multiclass-and-multilabel-classification-strategies" title="Permalink to this headline">¶</a></h3>
<dl class="simple">
<dt>This module implements multiclass learning algorithms:</dt><dd><ul class="simple">
<li><p>one-vs-the-rest / one-vs-all</p></li>
<li><p>one-vs-one</p></li>
<li><p>error correcting output codes</p></li>
</ul>
</dd>
</dl>
<p>The estimators provided in this module are meta-estimators: they require a base
estimator to be provided in their constructor. For example, it is possible to
use these estimators to turn a binary classifier or a regressor into a
multiclass classifier. It is also possible to use these estimators with
multiclass estimators in the hope that their accuracy or runtime performance
improves.</p>
<p>All classifiers in scikit-learn implement multiclass classification; you
only need to use this module if you want to experiment with custom multiclass
strategies.</p>
<p>The one-vs-the-rest meta-classifier also implements a <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method,
so long as such a method is implemented by the base classifier. This method
returns probabilities of class membership in both the single label and
multilabel case.  Note that in the multilabel case, probabilities are the
marginal probability that a given sample falls in the given class. As such, in
the multilabel case the sum of these probabilities over all possible labels
for a given sample <em>will not</em> sum to unity, as they do in the single label
case.</p>
</div>
<p><strong>User guide:</strong> See the <a class="reference internal" href="multiclass.html#multiclass"><span class="std std-ref">Multiclass and multilabel algorithms</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multiclass.OneVsRestClassifier</span></code></a>(estimator[, …])</p></td>
<td><p>One-vs-the-rest (OvR) multiclass/multilabel strategy</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.multiclass.OneVsOneClassifier.html#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multiclass.OneVsOneClassifier</span></code></a>(estimator[, …])</p></td>
<td><p>One-vs-one multiclass strategy</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.multiclass.OutputCodeClassifier.html#sklearn.multiclass.OutputCodeClassifier" title="sklearn.multiclass.OutputCodeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multiclass.OutputCodeClassifier</span></code></a>(estimator[, …])</p></td>
<td><p>(Error-Correcting) Output-Code multiclass strategy</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.multioutput">
<span id="sklearn-multioutput-multioutput-regression-and-classification"></span><span id="multioutput-ref"></span><h2><a class="reference internal" href="#module-sklearn.multioutput" title="sklearn.multioutput"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multioutput</span></code></a>: Multioutput regression and classification<a class="headerlink" href="#module-sklearn.multioutput" title="Permalink to this headline">¶</a></h2>
<p>This module implements multioutput regression and classification.</p>
<p>The estimators provided in this module are meta-estimators: they require
a base estimator to be provided in their constructor. The meta-estimator
extends single output estimators to multioutput estimators.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="multiclass.html#multiclass"><span class="std std-ref">Multiclass and multilabel algorithms</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.multioutput.ClassifierChain.html#sklearn.multioutput.ClassifierChain" title="sklearn.multioutput.ClassifierChain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multioutput.ClassifierChain</span></code></a>(base_estimator)</p></td>
<td><p>A multi-label model that arranges binary classifiers into a chain.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multioutput.MultiOutputRegressor</span></code></a>(estimator)</p></td>
<td><p>Multi target regression</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.multioutput.MultiOutputClassifier.html#sklearn.multioutput.MultiOutputClassifier" title="sklearn.multioutput.MultiOutputClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multioutput.MultiOutputClassifier</span></code></a>(estimator)</p></td>
<td><p>Multi target classification</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.multioutput.RegressorChain.html#sklearn.multioutput.RegressorChain" title="sklearn.multioutput.RegressorChain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multioutput.RegressorChain</span></code></a>(base_estimator[, …])</p></td>
<td><p>A multi-label model that arranges regressions into a chain.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.naive_bayes">
<span id="sklearn-naive-bayes-naive-bayes"></span><span id="naive-bayes-ref"></span><h2><a class="reference internal" href="#module-sklearn.naive_bayes" title="sklearn.naive_bayes"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.naive_bayes</span></code></a>: Naive Bayes<a class="headerlink" href="#module-sklearn.naive_bayes" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.naive_bayes" title="sklearn.naive_bayes"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.naive_bayes</span></code></a> module implements Naive Bayes algorithms. These
are supervised learning methods based on applying Bayes’ theorem with strong
(naive) feature independence assumptions.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="naive_bayes.html#naive-bayes"><span class="std std-ref">Naive Bayes</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">naive_bayes.BernoulliNB</span></code></a>([alpha, binarize, …])</p></td>
<td><p>Naive Bayes classifier for multivariate Bernoulli models.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.naive_bayes.CategoricalNB.html#sklearn.naive_bayes.CategoricalNB" title="sklearn.naive_bayes.CategoricalNB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">naive_bayes.CategoricalNB</span></code></a>([alpha, …])</p></td>
<td><p>Naive Bayes classifier for categorical features</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.naive_bayes.ComplementNB.html#sklearn.naive_bayes.ComplementNB" title="sklearn.naive_bayes.ComplementNB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">naive_bayes.ComplementNB</span></code></a>([alpha, fit_prior, …])</p></td>
<td><p>The Complement Naive Bayes classifier described in Rennie et al.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">naive_bayes.GaussianNB</span></code></a>([priors, var_smoothing])</p></td>
<td><p>Gaussian Naive Bayes (GaussianNB)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB" title="sklearn.naive_bayes.MultinomialNB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">naive_bayes.MultinomialNB</span></code></a>([alpha, …])</p></td>
<td><p>Naive Bayes classifier for multinomial models</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.neighbors">
<span id="sklearn-neighbors-nearest-neighbors"></span><span id="neighbors-ref"></span><h2><a class="reference internal" href="#module-sklearn.neighbors" title="sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code></a>: Nearest Neighbors<a class="headerlink" href="#module-sklearn.neighbors" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.neighbors" title="sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code></a> module implements the k-nearest neighbors
algorithm.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="neighbors.html#neighbors"><span class="std std-ref">Nearest Neighbors</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.BallTree</span></code></a></p></td>
<td><p>BallTree for fast generalized N-point problems</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neighbors.DistanceMetric.html#sklearn.neighbors.DistanceMetric" title="sklearn.neighbors.DistanceMetric"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.DistanceMetric</span></code></a></p></td>
<td><p>DistanceMetric class</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.KDTree</span></code></a></p></td>
<td><p>KDTree for fast generalized N-point problems</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neighbors.KernelDensity.html#sklearn.neighbors.KernelDensity" title="sklearn.neighbors.KernelDensity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.KernelDensity</span></code></a>([bandwidth, …])</p></td>
<td><p>Kernel Density Estimation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.KNeighborsClassifier</span></code></a>([…])</p></td>
<td><p>Classifier implementing the k-nearest neighbors vote.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor" title="sklearn.neighbors.KNeighborsRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.KNeighborsRegressor</span></code></a>([n_neighbors, …])</p></td>
<td><p>Regression based on k-nearest neighbors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neighbors.KNeighborsTransformer.html#sklearn.neighbors.KNeighborsTransformer" title="sklearn.neighbors.KNeighborsTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.KNeighborsTransformer</span></code></a>([mode, …])</p></td>
<td><p>Transform X into a (weighted) graph of k nearest neighbors</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor" title="sklearn.neighbors.LocalOutlierFactor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.LocalOutlierFactor</span></code></a>([n_neighbors, …])</p></td>
<td><p>Unsupervised Outlier Detection using Local Outlier Factor (LOF)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neighbors.RadiusNeighborsClassifier.html#sklearn.neighbors.RadiusNeighborsClassifier" title="sklearn.neighbors.RadiusNeighborsClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.RadiusNeighborsClassifier</span></code></a>([…])</p></td>
<td><p>Classifier implementing a vote among neighbors within a given radius</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neighbors.RadiusNeighborsRegressor.html#sklearn.neighbors.RadiusNeighborsRegressor" title="sklearn.neighbors.RadiusNeighborsRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.RadiusNeighborsRegressor</span></code></a>([radius, …])</p></td>
<td><p>Regression based on neighbors within a fixed radius.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neighbors.RadiusNeighborsTransformer.html#sklearn.neighbors.RadiusNeighborsTransformer" title="sklearn.neighbors.RadiusNeighborsTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.RadiusNeighborsTransformer</span></code></a>([mode, …])</p></td>
<td><p>Transform X into a (weighted) graph of neighbors nearer than a radius</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neighbors.NearestCentroid.html#sklearn.neighbors.NearestCentroid" title="sklearn.neighbors.NearestCentroid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.NearestCentroid</span></code></a>([metric, …])</p></td>
<td><p>Nearest centroid classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors" title="sklearn.neighbors.NearestNeighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.NearestNeighbors</span></code></a>([n_neighbors, …])</p></td>
<td><p>Unsupervised learner for implementing neighbor searches.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.html#sklearn.neighbors.NeighborhoodComponentsAnalysis" title="sklearn.neighbors.NeighborhoodComponentsAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.NeighborhoodComponentsAnalysis</span></code></a>([…])</p></td>
<td><p>Neighborhood Components Analysis</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neighbors.kneighbors_graph.html#sklearn.neighbors.kneighbors_graph" title="sklearn.neighbors.kneighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.kneighbors_graph</span></code></a>(X, n_neighbors[, …])</p></td>
<td><p>Computes the (weighted) graph of k-Neighbors for points in X</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neighbors.radius_neighbors_graph.html#sklearn.neighbors.radius_neighbors_graph" title="sklearn.neighbors.radius_neighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors.radius_neighbors_graph</span></code></a>(X, radius)</p></td>
<td><p>Computes the (weighted) graph of Neighbors for points in X</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.neural_network">
<span id="sklearn-neural-network-neural-network-models"></span><span id="neural-network-ref"></span><h2><a class="reference internal" href="#module-sklearn.neural_network" title="sklearn.neural_network"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neural_network</span></code></a>: Neural network models<a class="headerlink" href="#module-sklearn.neural_network" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.neural_network" title="sklearn.neural_network"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neural_network</span></code></a> module includes models based on neural
networks.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="neural_networks_supervised.html#neural-networks-supervised"><span class="std std-ref">Neural network models (supervised)</span></a> and <a class="reference internal" href="neural_networks_unsupervised.html#neural-networks-unsupervised"><span class="std std-ref">Neural network models (unsupervised)</span></a> sections for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neural_network.BernoulliRBM.html#sklearn.neural_network.BernoulliRBM" title="sklearn.neural_network.BernoulliRBM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neural_network.BernoulliRBM</span></code></a>([n_components, …])</p></td>
<td><p>Bernoulli Restricted Boltzmann Machine (RBM).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network.MLPClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neural_network.MLPClassifier</span></code></a>([…])</p></td>
<td><p>Multi-layer Perceptron classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network.MLPRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neural_network.MLPRegressor</span></code></a>([…])</p></td>
<td><p>Multi-layer Perceptron regressor.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.pipeline">
<span id="sklearn-pipeline-pipeline"></span><span id="pipeline-ref"></span><h2><a class="reference internal" href="#module-sklearn.pipeline" title="sklearn.pipeline"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.pipeline</span></code></a>: Pipeline<a class="headerlink" href="#module-sklearn.pipeline" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.pipeline" title="sklearn.pipeline"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.pipeline</span></code></a> module implements utilities to build a composite
estimator, as a chain of transforms and estimators.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pipeline.FeatureUnion</span></code></a>(transformer_list[, …])</p></td>
<td><p>Concatenates results of multiple transformer objects.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code></a>(steps[, memory, verbose])</p></td>
<td><p>Pipeline of transforms with a final estimator.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pipeline.make_pipeline</span></code></a>(\*steps, \*\*kwargs)</p></td>
<td><p>Construct a Pipeline from the given estimators.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.pipeline.make_union.html#sklearn.pipeline.make_union" title="sklearn.pipeline.make_union"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pipeline.make_union</span></code></a>(\*transformers, \*\*kwargs)</p></td>
<td><p>Construct a FeatureUnion from the given transformers.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.preprocessing">
<span id="sklearn-preprocessing-preprocessing-and-normalization"></span><span id="preprocessing-ref"></span><h2><a class="reference internal" href="#module-sklearn.preprocessing" title="sklearn.preprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code></a>: Preprocessing and Normalization<a class="headerlink" href="#module-sklearn.preprocessing" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.preprocessing" title="sklearn.preprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code></a> module includes scaling, centering,
normalization, binarization methods.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="preprocessing.html#preprocessing"><span class="std std-ref">Preprocessing data</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.Binarizer.html#sklearn.preprocessing.Binarizer" title="sklearn.preprocessing.Binarizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.Binarizer</span></code></a>([threshold, copy])</p></td>
<td><p>Binarize data (set feature values to 0 or 1) according to a threshold</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.FunctionTransformer</span></code></a>([func, …])</p></td>
<td><p>Constructs a transformer from an arbitrary callable.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.KBinsDiscretizer</span></code></a>([n_bins, …])</p></td>
<td><p>Bin continuous data into intervals.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.KernelCenterer.html#sklearn.preprocessing.KernelCenterer" title="sklearn.preprocessing.KernelCenterer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.KernelCenterer</span></code></a>()</p></td>
<td><p>Center a kernel matrix</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.LabelBinarizer.html#sklearn.preprocessing.LabelBinarizer" title="sklearn.preprocessing.LabelBinarizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.LabelBinarizer</span></code></a>([neg_label, …])</p></td>
<td><p>Binarize labels in a one-vs-all fashion</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.LabelEncoder</span></code></a></p></td>
<td><p>Encode target labels with value between 0 and n_classes-1.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.MultiLabelBinarizer.html#sklearn.preprocessing.MultiLabelBinarizer" title="sklearn.preprocessing.MultiLabelBinarizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.MultiLabelBinarizer</span></code></a>([classes, …])</p></td>
<td><p>Transform between iterable of iterables and a multilabel format</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler" title="sklearn.preprocessing.MaxAbsScaler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.MaxAbsScaler</span></code></a>([copy])</p></td>
<td><p>Scale each feature by its maximum absolute value.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.MinMaxScaler</span></code></a>([feature_range, copy])</p></td>
<td><p>Transform features by scaling each feature to a given range.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer" title="sklearn.preprocessing.Normalizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.Normalizer</span></code></a>([norm, copy])</p></td>
<td><p>Normalize samples individually to unit norm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.OneHotEncoder</span></code></a>([categories, …])</p></td>
<td><p>Encode categorical features as a one-hot numeric array.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.OrdinalEncoder</span></code></a>([categories, dtype])</p></td>
<td><p>Encode categorical features as an integer array.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures" title="sklearn.preprocessing.PolynomialFeatures"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.PolynomialFeatures</span></code></a>([degree, …])</p></td>
<td><p>Generate polynomial and interaction features.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.PowerTransformer.html#sklearn.preprocessing.PowerTransformer" title="sklearn.preprocessing.PowerTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.PowerTransformer</span></code></a>([method, …])</p></td>
<td><p>Apply a power transform featurewise to make data more Gaussian-like.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer" title="sklearn.preprocessing.QuantileTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.QuantileTransformer</span></code></a>([…])</p></td>
<td><p>Transform features using quantiles information.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.RobustScaler.html#sklearn.preprocessing.RobustScaler" title="sklearn.preprocessing.RobustScaler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.RobustScaler</span></code></a>([with_centering, …])</p></td>
<td><p>Scale features using statistics that are robust to outliers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.StandardScaler</span></code></a>([copy, …])</p></td>
<td><p>Standardize features by removing the mean and scaling to unit variance</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.add_dummy_feature.html#sklearn.preprocessing.add_dummy_feature" title="sklearn.preprocessing.add_dummy_feature"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.add_dummy_feature</span></code></a>(X[, value])</p></td>
<td><p>Augment dataset with an additional dummy feature.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.binarize.html#sklearn.preprocessing.binarize" title="sklearn.preprocessing.binarize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.binarize</span></code></a>(X[, threshold, copy])</p></td>
<td><p>Boolean thresholding of array-like or scipy.sparse matrix</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.label_binarize.html#sklearn.preprocessing.label_binarize" title="sklearn.preprocessing.label_binarize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.label_binarize</span></code></a>(y, classes[, …])</p></td>
<td><p>Binarize labels in a one-vs-all fashion</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.maxabs_scale.html#sklearn.preprocessing.maxabs_scale" title="sklearn.preprocessing.maxabs_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.maxabs_scale</span></code></a>(X[, axis, copy])</p></td>
<td><p>Scale each feature to the [-1, 1] range without breaking the sparsity.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.minmax_scale.html#sklearn.preprocessing.minmax_scale" title="sklearn.preprocessing.minmax_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.minmax_scale</span></code></a>(X[, …])</p></td>
<td><p>Transform features by scaling each feature to a given range.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.normalize.html#sklearn.preprocessing.normalize" title="sklearn.preprocessing.normalize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.normalize</span></code></a>(X[, norm, axis, …])</p></td>
<td><p>Scale input vectors individually to unit norm (vector length).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.quantile_transform.html#sklearn.preprocessing.quantile_transform" title="sklearn.preprocessing.quantile_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.quantile_transform</span></code></a>(X[, axis, …])</p></td>
<td><p>Transform features using quantiles information.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.robust_scale.html#sklearn.preprocessing.robust_scale" title="sklearn.preprocessing.robust_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.robust_scale</span></code></a>(X[, axis, …])</p></td>
<td><p>Standardize a dataset along any axis</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.scale</span></code></a>(X[, axis, with_mean, …])</p></td>
<td><p>Standardize a dataset along any axis</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.preprocessing.power_transform.html#sklearn.preprocessing.power_transform" title="sklearn.preprocessing.power_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocessing.power_transform</span></code></a>(X[, method, …])</p></td>
<td><p>Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.random_projection">
<span id="sklearn-random-projection-random-projection"></span><span id="random-projection-ref"></span><h2><a class="reference internal" href="#module-sklearn.random_projection" title="sklearn.random_projection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.random_projection</span></code></a>: Random projection<a class="headerlink" href="#module-sklearn.random_projection" title="Permalink to this headline">¶</a></h2>
<p>Random Projection transformers</p>
<p>Random Projections are a simple and computationally efficient way to
reduce the dimensionality of the data by trading a controlled amount
of accuracy (as additional variance) for faster processing times and
smaller model sizes.</p>
<p>The dimensions and distribution of Random Projections matrices are
controlled so as to preserve the pairwise distances between any two
samples of the dataset.</p>
<p>The main theoretical result behind the efficiency of random projection is the
<a class="reference external" href="https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma">Johnson-Lindenstrauss lemma (quoting Wikipedia)</a>:</p>
<blockquote>
<div><p>In mathematics, the Johnson-Lindenstrauss lemma is a result
concerning low-distortion embeddings of points from high-dimensional
into low-dimensional Euclidean space. The lemma states that a small set
of points in a high-dimensional space can be embedded into a space of
much lower dimension in such a way that distances between the points are
nearly preserved. The map used for the embedding is at least Lipschitz,
and can even be taken to be an orthogonal projection.</p>
</div></blockquote>
<p><strong>User guide:</strong> See the <a class="reference internal" href="random_projection.html#random-projection"><span class="std std-ref">Random Projection</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.random_projection.GaussianRandomProjection.html#sklearn.random_projection.GaussianRandomProjection" title="sklearn.random_projection.GaussianRandomProjection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_projection.GaussianRandomProjection</span></code></a>([…])</p></td>
<td><p>Reduce dimensionality through Gaussian random projection</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.random_projection.SparseRandomProjection.html#sklearn.random_projection.SparseRandomProjection" title="sklearn.random_projection.SparseRandomProjection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_projection.SparseRandomProjection</span></code></a>([…])</p></td>
<td><p>Reduce dimensionality through sparse random projection</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.random_projection.johnson_lindenstrauss_min_dim.html#sklearn.random_projection.johnson_lindenstrauss_min_dim" title="sklearn.random_projection.johnson_lindenstrauss_min_dim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_projection.johnson_lindenstrauss_min_dim</span></code></a>(…)</p></td>
<td><p>Find a ‘safe’ number of components to randomly project to</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.semi_supervised">
<span id="sklearn-semi-supervised-semi-supervised-learning"></span><span id="semi-supervised-ref"></span><h2><a class="reference internal" href="#module-sklearn.semi_supervised" title="sklearn.semi_supervised"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.semi_supervised</span></code></a> Semi-Supervised Learning<a class="headerlink" href="#module-sklearn.semi_supervised" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.semi_supervised" title="sklearn.semi_supervised"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.semi_supervised</span></code></a> module implements semi-supervised learning
algorithms. These algorithms utilized small amounts of labeled data and large
amounts of unlabeled data for classification tasks. This module includes Label
Propagation.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="label_propagation.html#semi-supervised"><span class="std std-ref">Semi-Supervised</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.semi_supervised.LabelPropagation.html#sklearn.semi_supervised.LabelPropagation" title="sklearn.semi_supervised.LabelPropagation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">semi_supervised.LabelPropagation</span></code></a>([kernel, …])</p></td>
<td><p>Label Propagation classifier</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.semi_supervised.LabelSpreading.html#sklearn.semi_supervised.LabelSpreading" title="sklearn.semi_supervised.LabelSpreading"><code class="xref py py-obj docutils literal notranslate"><span class="pre">semi_supervised.LabelSpreading</span></code></a>([kernel, …])</p></td>
<td><p>LabelSpreading model for semi-supervised learning</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.svm">
<span id="sklearn-svm-support-vector-machines"></span><span id="svm-ref"></span><h2><a class="reference internal" href="#module-sklearn.svm" title="sklearn.svm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.svm</span></code></a>: Support Vector Machines<a class="headerlink" href="#module-sklearn.svm" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.svm" title="sklearn.svm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.svm</span></code></a> module includes Support Vector Machine algorithms.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="svm.html#svm"><span class="std std-ref">Support Vector Machines</span></a> section for further details.</p>
<div class="section" id="estimators">
<h3>Estimators<a class="headerlink" href="#estimators" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svm.LinearSVC</span></code></a>([penalty, loss, dual, tol, C, …])</p></td>
<td><p>Linear Support Vector Classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.svm.LinearSVR.html#sklearn.svm.LinearSVR" title="sklearn.svm.LinearSVR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svm.LinearSVR</span></code></a>([epsilon, tol, C, loss, …])</p></td>
<td><p>Linear Support Vector Regression.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC" title="sklearn.svm.NuSVC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svm.NuSVC</span></code></a>([nu, kernel, degree, gamma, …])</p></td>
<td><p>Nu-Support Vector Classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.svm.NuSVR.html#sklearn.svm.NuSVR" title="sklearn.svm.NuSVR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svm.NuSVR</span></code></a>([nu, C, kernel, degree, gamma, …])</p></td>
<td><p>Nu Support Vector Regression.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM" title="sklearn.svm.OneClassSVM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svm.OneClassSVM</span></code></a>([kernel, degree, gamma, …])</p></td>
<td><p>Unsupervised Outlier Detection.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svm.SVC</span></code></a>([C, kernel, degree, gamma, coef0, …])</p></td>
<td><p>C-Support Vector Classification.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.svm.SVR.html#sklearn.svm.SVR" title="sklearn.svm.SVR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svm.SVR</span></code></a>([kernel, degree, gamma, coef0, tol, …])</p></td>
<td><p>Epsilon-Support Vector Regression.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.svm.l1_min_c.html#sklearn.svm.l1_min_c" title="sklearn.svm.l1_min_c"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svm.l1_min_c</span></code></a>(X, y[, loss, fit_intercept, …])</p></td>
<td><p>Return the lowest bound for C such that for C in (l1_min_C, infinity) the model is guaranteed not to be empty.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.tree">
<span id="sklearn-tree-decision-trees"></span><span id="tree-ref"></span><h2><a class="reference internal" href="#module-sklearn.tree" title="sklearn.tree"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.tree</span></code></a>: Decision Trees<a class="headerlink" href="#module-sklearn.tree" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.tree" title="sklearn.tree"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.tree</span></code></a> module includes decision tree-based models for
classification and regression.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="tree.html#tree"><span class="std std-ref">Decision Trees</span></a> section for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tree.DecisionTreeClassifier</span></code></a>([criterion, …])</p></td>
<td><p>A decision tree classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tree.DecisionTreeRegressor</span></code></a>([criterion, …])</p></td>
<td><p>A decision tree regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.tree.ExtraTreeClassifier.html#sklearn.tree.ExtraTreeClassifier" title="sklearn.tree.ExtraTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tree.ExtraTreeClassifier</span></code></a>([criterion, …])</p></td>
<td><p>An extremely randomized tree classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.tree.ExtraTreeRegressor.html#sklearn.tree.ExtraTreeRegressor" title="sklearn.tree.ExtraTreeRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tree.ExtraTreeRegressor</span></code></a>([criterion, …])</p></td>
<td><p>An extremely randomized tree regressor.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.tree.export_graphviz.html#sklearn.tree.export_graphviz" title="sklearn.tree.export_graphviz"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tree.export_graphviz</span></code></a>(decision_tree[, …])</p></td>
<td><p>Export a decision tree in DOT format.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.tree.export_text.html#sklearn.tree.export_text" title="sklearn.tree.export_text"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tree.export_text</span></code></a>(decision_tree[, …])</p></td>
<td><p>Build a text report showing the rules of a decision tree.</p></td>
</tr>
</tbody>
</table>
<div class="section" id="id4">
<h3>Plotting<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.tree.plot_tree.html#sklearn.tree.plot_tree" title="sklearn.tree.plot_tree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tree.plot_tree</span></code></a>(decision_tree[, max_depth, …])</p></td>
<td><p>Plot a decision tree.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.utils">
<span id="sklearn-utils-utilities"></span><span id="utils-ref"></span><h2><a class="reference internal" href="#module-sklearn.utils" title="sklearn.utils"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.utils</span></code></a>: Utilities<a class="headerlink" href="#module-sklearn.utils" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.utils" title="sklearn.utils"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.utils</span></code></a> module includes various utilities.</p>
<p><strong>Developer guide:</strong> See the <a class="reference internal" href="../developers/utilities.html#developers-utils"><span class="std std-ref">Utilities for Developers</span></a> page for further details.</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.arrayfuncs.min_pos.html#sklearn.utils.arrayfuncs.min_pos" title="sklearn.utils.arrayfuncs.min_pos"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.arrayfuncs.min_pos</span></code></a>()</p></td>
<td><p>Find the minimum value of an array over positive values</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.as_float_array.html#sklearn.utils.as_float_array" title="sklearn.utils.as_float_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.as_float_array</span></code></a>(X[, copy, force_all_finite])</p></td>
<td><p>Converts an array-like to an array of floats.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.assert_all_finite.html#sklearn.utils.assert_all_finite" title="sklearn.utils.assert_all_finite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.assert_all_finite</span></code></a>(X[, allow_nan])</p></td>
<td><p>Throw a ValueError if X contains NaN or infinity.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.check_X_y.html#sklearn.utils.check_X_y" title="sklearn.utils.check_X_y"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_X_y</span></code></a>(X, y[, accept_sparse, …])</p></td>
<td><p>Input validation for standard estimators.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.check_array.html#sklearn.utils.check_array" title="sklearn.utils.check_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_array</span></code></a>(array[, accept_sparse, …])</p></td>
<td><p>Input validation on an array, list, sparse matrix or similar.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.check_scalar.html#sklearn.utils.check_scalar" title="sklearn.utils.check_scalar"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_scalar</span></code></a>(x, name, target_type[, …])</p></td>
<td><p>Validate scalar parameters type and value.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.check_consistent_length.html#sklearn.utils.check_consistent_length" title="sklearn.utils.check_consistent_length"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_consistent_length</span></code></a>(\*arrays)</p></td>
<td><p>Check that all arrays have consistent first dimensions.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.check_random_state.html#sklearn.utils.check_random_state" title="sklearn.utils.check_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_random_state</span></code></a>(seed)</p></td>
<td><p>Turn seed into a np.random.RandomState instance</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.class_weight.compute_class_weight.html#sklearn.utils.class_weight.compute_class_weight" title="sklearn.utils.class_weight.compute_class_weight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.class_weight.compute_class_weight</span></code></a>(…)</p></td>
<td><p>Estimate class weights for unbalanced datasets.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.class_weight.compute_sample_weight.html#sklearn.utils.class_weight.compute_sample_weight" title="sklearn.utils.class_weight.compute_sample_weight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.class_weight.compute_sample_weight</span></code></a>(…)</p></td>
<td><p>Estimate sample weights by class for unbalanced datasets.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.deprecated.html#sklearn.utils.deprecated" title="sklearn.utils.deprecated"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.deprecated</span></code></a>([extra])</p></td>
<td><p>Decorator to mark a function or class as deprecated.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.estimator_checks.check_estimator.html#sklearn.utils.estimator_checks.check_estimator" title="sklearn.utils.estimator_checks.check_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.estimator_checks.check_estimator</span></code></a>(Estimator)</p></td>
<td><p>Check if estimator adheres to scikit-learn conventions.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.estimator_checks.parametrize_with_checks.html#sklearn.utils.estimator_checks.parametrize_with_checks" title="sklearn.utils.estimator_checks.parametrize_with_checks"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.estimator_checks.parametrize_with_checks</span></code></a>(…)</p></td>
<td><p>Pytest specific decorator for parametrizing estimator checks.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.extmath.safe_sparse_dot.html#sklearn.utils.extmath.safe_sparse_dot" title="sklearn.utils.extmath.safe_sparse_dot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.extmath.safe_sparse_dot</span></code></a>(a, b[, …])</p></td>
<td><p>Dot product that handle the sparse matrix case correctly</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.extmath.randomized_range_finder.html#sklearn.utils.extmath.randomized_range_finder" title="sklearn.utils.extmath.randomized_range_finder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.extmath.randomized_range_finder</span></code></a>(A, …)</p></td>
<td><p>Computes an orthonormal matrix whose range approximates the range of A.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.extmath.randomized_svd.html#sklearn.utils.extmath.randomized_svd" title="sklearn.utils.extmath.randomized_svd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.extmath.randomized_svd</span></code></a>(M, n_components)</p></td>
<td><p>Computes a truncated randomized SVD</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.extmath.fast_logdet.html#sklearn.utils.extmath.fast_logdet" title="sklearn.utils.extmath.fast_logdet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.extmath.fast_logdet</span></code></a>(A)</p></td>
<td><p>Compute log(det(A)) for A symmetric</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.extmath.density.html#sklearn.utils.extmath.density" title="sklearn.utils.extmath.density"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.extmath.density</span></code></a>(w, \*\*kwargs)</p></td>
<td><p>Compute density of a sparse vector</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.extmath.weighted_mode.html#sklearn.utils.extmath.weighted_mode" title="sklearn.utils.extmath.weighted_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.extmath.weighted_mode</span></code></a>(a, w[, axis])</p></td>
<td><p>Returns an array of the weighted modal (most common) value in a</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.gen_even_slices.html#sklearn.utils.gen_even_slices" title="sklearn.utils.gen_even_slices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.gen_even_slices</span></code></a>(n, n_packs[, n_samples])</p></td>
<td><p>Generator to create n_packs slices going up to n.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.graph.single_source_shortest_path_length.html#sklearn.utils.graph.single_source_shortest_path_length" title="sklearn.utils.graph.single_source_shortest_path_length"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.graph.single_source_shortest_path_length</span></code></a>(…)</p></td>
<td><p>Return the shortest path length from source to all reachable nodes.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.graph_shortest_path.graph_shortest_path.html#sklearn.utils.graph_shortest_path.graph_shortest_path" title="sklearn.utils.graph_shortest_path.graph_shortest_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.graph_shortest_path.graph_shortest_path</span></code></a>()</p></td>
<td><p>Perform a shortest-path graph search on a positive directed or undirected graph.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.indexable.html#sklearn.utils.indexable" title="sklearn.utils.indexable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.indexable</span></code></a>(\*iterables)</p></td>
<td><p>Make arrays indexable for cross-validation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.metaestimators.if_delegate_has_method.html#sklearn.utils.metaestimators.if_delegate_has_method" title="sklearn.utils.metaestimators.if_delegate_has_method"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.metaestimators.if_delegate_has_method</span></code></a>(…)</p></td>
<td><p>Create a decorator for methods that are delegated to a sub-estimator</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.multiclass.type_of_target.html#sklearn.utils.multiclass.type_of_target" title="sklearn.utils.multiclass.type_of_target"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.multiclass.type_of_target</span></code></a>(y)</p></td>
<td><p>Determine the type of data indicated by the target.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.multiclass.is_multilabel.html#sklearn.utils.multiclass.is_multilabel" title="sklearn.utils.multiclass.is_multilabel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.multiclass.is_multilabel</span></code></a>(y)</p></td>
<td><p>Check if <code class="docutils literal notranslate"><span class="pre">y</span></code> is in a multilabel format.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.multiclass.unique_labels.html#sklearn.utils.multiclass.unique_labels" title="sklearn.utils.multiclass.unique_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.multiclass.unique_labels</span></code></a>(\*ys)</p></td>
<td><p>Extract an ordered array of unique labels</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.murmurhash3_32.html#sklearn.utils.murmurhash3_32" title="sklearn.utils.murmurhash3_32"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.murmurhash3_32</span></code></a>()</p></td>
<td><p>Compute the 32bit murmurhash3 of key at seed.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.resample.html#sklearn.utils.resample" title="sklearn.utils.resample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.resample</span></code></a>(\*arrays, \*\*options)</p></td>
<td><p>Resample arrays or sparse matrices in a consistent way</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils._safe_indexing.html#sklearn.utils._safe_indexing" title="sklearn.utils._safe_indexing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils._safe_indexing</span></code></a>(X, indices[, axis])</p></td>
<td><p>Return rows, items or columns of X using indices.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.safe_mask.html#sklearn.utils.safe_mask" title="sklearn.utils.safe_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.safe_mask</span></code></a>(X, mask)</p></td>
<td><p>Return a mask which is safe to use on X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.safe_sqr.html#sklearn.utils.safe_sqr" title="sklearn.utils.safe_sqr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.safe_sqr</span></code></a>(X[, copy])</p></td>
<td><p>Element wise squaring of array-likes and sparse matrices.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.shuffle.html#sklearn.utils.shuffle" title="sklearn.utils.shuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.shuffle</span></code></a>(\*arrays, \*\*options)</p></td>
<td><p>Shuffle arrays or sparse matrices in a consistent way</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html#sklearn.utils.sparsefuncs.incr_mean_variance_axis" title="sklearn.utils.sparsefuncs.incr_mean_variance_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs.incr_mean_variance_axis</span></code></a>(X, …)</p></td>
<td><p>Compute incremental mean and variance along an axix on a CSR or CSC matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs.inplace_column_scale.html#sklearn.utils.sparsefuncs.inplace_column_scale" title="sklearn.utils.sparsefuncs.inplace_column_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs.inplace_column_scale</span></code></a>(X, scale)</p></td>
<td><p>Inplace column scaling of a CSC/CSR matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs.inplace_row_scale.html#sklearn.utils.sparsefuncs.inplace_row_scale" title="sklearn.utils.sparsefuncs.inplace_row_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs.inplace_row_scale</span></code></a>(X, scale)</p></td>
<td><p>Inplace row scaling of a CSR or CSC matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs.inplace_swap_row.html#sklearn.utils.sparsefuncs.inplace_swap_row" title="sklearn.utils.sparsefuncs.inplace_swap_row"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs.inplace_swap_row</span></code></a>(X, m, n)</p></td>
<td><p>Swaps two rows of a CSC/CSR matrix in-place.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs.inplace_swap_column.html#sklearn.utils.sparsefuncs.inplace_swap_column" title="sklearn.utils.sparsefuncs.inplace_swap_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs.inplace_swap_column</span></code></a>(X, m, n)</p></td>
<td><p>Swaps two columns of a CSC/CSR matrix in-place.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs.mean_variance_axis.html#sklearn.utils.sparsefuncs.mean_variance_axis" title="sklearn.utils.sparsefuncs.mean_variance_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs.mean_variance_axis</span></code></a>(X, axis)</p></td>
<td><p>Compute mean and variance along an axix on a CSR or CSC matrix</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs.inplace_csr_column_scale.html#sklearn.utils.sparsefuncs.inplace_csr_column_scale" title="sklearn.utils.sparsefuncs.inplace_csr_column_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs.inplace_csr_column_scale</span></code></a>(X, …)</p></td>
<td><p>Inplace column scaling of a CSR matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1.html#sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1" title="sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs_fast.inplace_csr_row_normalize_l1</span></code></a>()</p></td>
<td><p>Inplace row normalize using the l1 norm</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2.html#sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2" title="sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.sparsefuncs_fast.inplace_csr_row_normalize_l2</span></code></a>()</p></td>
<td><p>Inplace row normalize using the l2 norm</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.random.sample_without_replacement.html#sklearn.utils.random.sample_without_replacement" title="sklearn.utils.random.sample_without_replacement"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.random.sample_without_replacement</span></code></a>()</p></td>
<td><p>Sample integers without replacement.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.validation.check_is_fitted.html#sklearn.utils.validation.check_is_fitted" title="sklearn.utils.validation.check_is_fitted"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.validation.check_is_fitted</span></code></a>(estimator)</p></td>
<td><p>Perform is_fitted validation for estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.validation.check_memory.html#sklearn.utils.validation.check_memory" title="sklearn.utils.validation.check_memory"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.validation.check_memory</span></code></a>(memory)</p></td>
<td><p>Check that <code class="docutils literal notranslate"><span class="pre">memory</span></code> is joblib.Memory-like.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.validation.check_symmetric.html#sklearn.utils.validation.check_symmetric" title="sklearn.utils.validation.check_symmetric"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.validation.check_symmetric</span></code></a>(array[, …])</p></td>
<td><p>Make sure that array is 2D, square and symmetric.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.validation.column_or_1d.html#sklearn.utils.validation.column_or_1d" title="sklearn.utils.validation.column_or_1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.validation.column_or_1d</span></code></a>(y[, warn])</p></td>
<td><p>Ravel column or 1d numpy array, else raises an error</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.validation.has_fit_parameter.html#sklearn.utils.validation.has_fit_parameter" title="sklearn.utils.validation.has_fit_parameter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.validation.has_fit_parameter</span></code></a>(…)</p></td>
<td><p>Checks whether the estimator’s fit method supports the given parameter.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.all_estimators.html#sklearn.utils.all_estimators" title="sklearn.utils.all_estimators"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.all_estimators</span></code></a>([…])</p></td>
<td><p>Get a list of all estimators from sklearn.</p></td>
</tr>
</tbody>
</table>
<p>Utilities from joblib:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.parallel_backend.html#sklearn.utils.parallel_backend" title="sklearn.utils.parallel_backend"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.parallel_backend</span></code></a>(backend[, n_jobs, …])</p></td>
<td><p>Change the default backend used by Parallel inside a with block.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.register_parallel_backend.html#sklearn.utils.register_parallel_backend" title="sklearn.utils.register_parallel_backend"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.register_parallel_backend</span></code></a>(name, factory)</p></td>
<td><p>Register a new Parallel backend factory.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="recently-deprecated">
<h2>Recently deprecated<a class="headerlink" href="#recently-deprecated" title="Permalink to this headline">¶</a></h2>
<div class="section" id="to-be-removed-in-0-23">
<h3>To be removed in 0.23<a class="headerlink" href="#to-be-removed-in-0-23" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.Memory.html#sklearn.utils.Memory" title="sklearn.utils.Memory"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.Memory</span></code></a>(**kwargs)</p></td>
<td><p><dl class="field-list simple">
<dt class="field-odd">Attributes</dt>
<dd class="field-odd"><p></p></dd>
</dl>
</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.Parallel.html#sklearn.utils.Parallel" title="sklearn.utils.Parallel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.Parallel</span></code></a>(**kwargs)</p></td>
<td><p><p class="rubric">Methods</p>
</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.utils.cpu_count.html#sklearn.utils.cpu_count" title="sklearn.utils.cpu_count"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.cpu_count</span></code></a>()</p></td>
<td><p>DEPRECATED: deprecated in version 0.20.1 to be removed in version 0.23.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.delayed.html#sklearn.utils.delayed" title="sklearn.utils.delayed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.delayed</span></code></a>(function[, check_pickle])</p></td>
<td><p>DEPRECATED: deprecated in version 0.20.1 to be removed in version 0.23.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.metrics.calinski_harabaz_score.html#sklearn.metrics.calinski_harabaz_score" title="sklearn.metrics.calinski_harabaz_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.calinski_harabaz_score</span></code></a>(X, labels)</p></td>
<td><p>DEPRECATED: Function ‘calinski_harabaz_score’ has been renamed to ‘calinski_harabasz_score’ and will be removed in version 0.23.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.metrics.jaccard_similarity_score.html#sklearn.metrics.jaccard_similarity_score" title="sklearn.metrics.jaccard_similarity_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.jaccard_similarity_score</span></code></a>(y_true, y_pred)</p></td>
<td><p>Jaccard similarity coefficient score</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.linear_model.logistic_regression_path.html#sklearn.linear_model.logistic_regression_path" title="sklearn.linear_model.logistic_regression_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear_model.logistic_regression_path</span></code></a>(X, y)</p></td>
<td><p>DEPRECATED: logistic_regression_path was deprecated in version 0.21 and will be removed in version 0.23.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.utils.safe_indexing.html#sklearn.utils.safe_indexing" title="sklearn.utils.safe_indexing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.safe_indexing</span></code></a>(X, indices[, axis])</p></td>
<td><p>DEPRECATED: safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.</p></td>
</tr>
</tbody>
</table>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.partial_dependence.partial_dependence.html#sklearn.ensemble.partial_dependence.partial_dependence" title="sklearn.ensemble.partial_dependence.partial_dependence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.partial_dependence.partial_dependence</span></code></a>(…)</p></td>
<td><p>DEPRECATED: The function ensemble.partial_dependence has been deprecated in favour of inspection.partial_dependence in 0.21 and will be removed in 0.23.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.partial_dependence.plot_partial_dependence.html#sklearn.ensemble.partial_dependence.plot_partial_dependence" title="sklearn.ensemble.partial_dependence.plot_partial_dependence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.partial_dependence.plot_partial_dependence</span></code></a>(…)</p></td>
<td><p>DEPRECATED: The function ensemble.plot_partial_dependence has been deprecated in favour of sklearn.inspection.plot_partial_dependence in  0.21 and will be removed in 0.23.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
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